{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b52a49c9",
   "metadata": {},
   "source": [
    "# A Simple Example\n",
    "1. datasets: iris\n",
    "2. classification model: DecisionTrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8cfa0264",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# prepare iris dataset\n",
    "iris_ex = load_iris()\n",
    "iris_ex.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "45dfe4c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['setosa' 'versicolor' 'virginica']\n",
      "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
     ]
    }
   ],
   "source": [
    "print(iris_ex[\"target_names\"])\n",
    "print(iris_ex[\"feature_names\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ecbad4e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=12)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_ex = iris_ex.data[:,2:] # petal length and width\n",
    "y_ex = iris_ex.target\n",
    "\n",
    "tree_ex_2_clf = DecisionTreeClassifier(max_depth=2)\n",
    "tree_ex_2_clf.fit(X_ex, y_ex)\n",
    "tree_ex_3_clf = DecisionTreeClassifier(max_depth=3)\n",
    "tree_ex_3_clf.fit(X_ex, y_ex)\n",
    "tree_ex_5_clf = DecisionTreeClassifier(max_depth=5)\n",
    "tree_ex_5_clf.fit(X_ex, y_ex)\n",
    "tree_ex_12_clf = DecisionTreeClassifier(max_depth=12)\n",
    "tree_ex_12_clf.fit(X_ex, y_ex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4572a511",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6.9 2.5]\n",
      "[1.  0.1]\n"
     ]
    }
   ],
   "source": [
    "print(X_ex.max(axis=0))\n",
    "print(X_ex.min(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f4643061",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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u3m1zzs6x/2RJiX8/CTGZTBgMhlj/fqy9l7NmzYrXW2bMxm7cf2smk8nuLtZT8zhFJHF6ZkkkE4gO6z///NMy7ezZs7z44ovs3r3bpm2sWbOGwMBAfvjhB3r27EnNmjXjnQGdNGkSJ0+epH379kybNo0VK1Zw4MABy616f/zxBzNmzOCll17i448/5s8//6RYsWJpOmjrM888w/79+2M9G/Tll1/Spk0bjEYjBw8eZMeOHcycOZOBAwfSsGFDLl26ZHVb7u7udne4AI9u24rZScGlS5c4duxYrCsRKfGH7Pbt2y3/H/27LlGiBPny5aNIkSKxajCZTLFuK3J2do51hcdkMrF69ep4+0iozuLFi2M0GmN9xmLWA/DNN9/QsmVLpk2bRrt27ciRIwe3b9+2+zgfp1y5cly5coVnnnnG8rN+/Xo2btwIPLrN0t/fnwEDBvDTTz/xwgsv2PUskJ+fH2vWrKFUqVK4ubnh6enJ1q1b7b6y5O7uDhCr45SYkvOZiP6jOubvI2aHAmkhutOIsLAwy+8iX758TJkyhXPnztm0jXLlynH+/HnKlClj2cbx48eZO3durOUSe6+ix2ZL6L1+nBIlSvDw4cNYt0DG/GyXKFECNzc3goODLTWWKVOG6dOnc+DAActyMU82BQYGcvz48XhX0xL7nkmvBq+IRNKVJZFMoFSpUrz22msMGjSIiIgIfH19+eyzz3jyySdtPvOdO3duHj58yPz58y331a9YsSLWbSSnT59m8eLFDBs2jFy5cjFv3jycnJwoUqQIAC4uLowYMQJnZ2eee+45jh07xrlz52I9w5Launfvzty5c2nXrh3vv/8+ly9fZvbs2XTt2hWIfD7AYDCwcuVKXFxc+P3335k0aRIQ2YNdzLPXzz33HEuWLKFcuXLcuHEDNzc3m3rUKl68OB07dmTw4MGYTCbL80M+Pj506tQpRY936NChlj8Khw8fHus2ro8++ogePXpQuHBhnn/+eb777juOHTvG7NmzgcjnqMaOHcuuXbsoU6YMQ4cO5dq1a2TLls2mfXt5edG2bVvGjx9Prly58PLyindLUu7cufn333/5/fffuXr1KuPHjycoKMjmnuhsMXDgQJo2bcrw4cNp0KAB27dvZ+zYsZZewzw9PZk2bRrOzs40aNCAy5cvW3pbs5Wfnx9hYWGWjidKlSrFmTNnHjuwqzVlypTB09OTzz//nDp16nDw4EFatGhB/vz57dqONU8//TT+/v70798fs9nMvXv34nW4kdqKFy9Ou3bt6NevH0FBQfj4+DBhwgQOHz4cq7OYx+nRowezZ8+mc+fOvPPOO1y/fp0PPviANm3a2FVLgQIFKFy4MLNmzaJNmzYcP36cqlWrWu0FMK4GDRpQokQJ3n33XT7++GPOnz9v+XcDkC1bNvr168eYMWNwc3PjqaeeYu7cuWzYsIGPPvrIstz27dsZMmQI9evXZ9asWRiNxnjfAc899xxDhw7l559/xmAwcOHCBcuzjiKS/nRlSSST+PLLL2nbti0fffQRHTt2xMvLi3Xr1iXY5XVcrVq1okePHowYMYK33nqLiIgIBg4cyKFDh7h79y4Q2ftTzZo16devH02bNmXXrl18++23lttratSowYwZM/juu+945ZVXGDZsGJ06deL9999PteOOy9fX19KVc+vWrRk9ejQ9e/a0/AFTqFAhpk2bxi+//MLrr7/Otm3bmDx5MhD7LDDAJ598gre3N82bN+fdd9+N1UlEYqZNm0bXrl0tnR9E1+Xl5ZUyBxrlww8/ZMCAAbz33nvUrVs31h907du354svvmDVqlW0aNGC48ePs27dOssf+I0aNaJr1640a9aMp59+GicnJ4YOHWrX/j///HOaNm3KgAED6NWrF/369Ys1f+LEieTLl4/WrVszfvx4OnfujJ+fX7z3Ojnq1avHvHnzWL9+Pc2aNWPx4sXMmjXL0slBsWLFWLx4MZs2baJFixb06dOHF154gTFjxti8j0qVKmE0GmM1lkqUKGFzJxHRvLy8+Oabb1i6dClNmjRh2rRpVp8TS6rvvvuOSpUq0b17d4YNG0b//v1TbNu2mjp1Ks2aNWPAgAG0bt0aJycn1q9fj6enp03r58mTh/Xr13P37l1atmzJ4MGD6dy5s12/r2jfffcd//zzD40bN2bUqFGW8aES4+LiwurVq8mVKxcdO3Zk5syZ8d7LwYMH8+677zJu3DhatGjB+fPnWbNmTazOJHr27MnevXt5/fXXOX36NMuWLYt3e2KHDh1o2bIlXbp0oU2bNvGGYxCR9GUICQlJuaeaRURERISyZcvyzjvv0Ldv3/QuRUSSQVeWRERERERErNCVJRERERERESt0ZUlERERERMQKNZZERERERESsUGNJRERERETECjWWRERERERErFBjSURERERExAo1lkRERERERKxQY0lERERERMQKNZZERERERESsUGNJRERERETECjWWRERERERErFBjSURERERExAo1lkRERERERKxQY0lERERERMQKNZaykLNnz+Lh4cHZs2dTfNsNGzZk9OjRSV4/NWvLisqWLcv8+fPtXi+5v0eAmzdv0rlzZwoUKMATTzxBw4YN0+T36uHhwR9//JHq+4mW1PdYROyj7Mo60jO7APbs2UO/fv3o2LEjEyZMICQkJNnbTIyyy/GpsSQpYurUqXTp0iXJ6/v4+LB161Z8fHxSsCr7/PHHH5nmC2T58uU0btzY6ryzZ8+mSKgkpGPHjmzbto2BAwcyatQoDh48SPv27VNtf6lp7969TJ8+Pb3LEJFUouxyLOmZXX/++ScNGjQgJCSEJ598kpkzZ9K8eXPMZnOq7TO1KLtSlhpLkiJKly6drLBwdXXFz88PV1fXFKzKPlu3bmXhwoXptv+UVL58eXLnzm113tmzZxkzZkyq7Pe3335j586d/Prrr/Tt25devXoxatQo9uzZw5kzZ1Jln6lp3759zJgxI73LEJFUouxyLOmVXQD9+vXjgw8+YM6cOYwYMYKFCxeyfft29u3bl2r7TC3KrpTlnN4FiEjmUblyZbZs2ULRokUt03LlygVAWFhYepUlIiKSoNDQUN577z2aN29umRbdiFZ2ia4sZWLXrl2jTZs25M6dm/Lly/Pbb7/FW+aLL76gfPny5M2blwYNGvDff//Fmr9t2zbq1q1Lrly5qFChAjNnzrS6r4TuFw4NDSUgIIASJUqQN29e6tWrx86dO+Mt97j7vq9evUrnzp3Jnz8/RYsWpX///oSGhlrme3h48OuvvzJgwAAKFSpE0aJF+eyzzxJ7e2LV7uHhwZgxY9i6dSseHh54eHjEuq0hZn3z58+natWqvPvuu7G2s3nzZurUqUOuXLnw8/Nj2bJlseafOXOGNm3akC9fPkqVKsXw4cMJDw+3qcaJEydSqlQpAObNm4eHhwc7d+7k9u3beHh48NNPP8Va3to9yaNHj8bDw4NGjRoBWI6ze/fu8fY3b948ypYtS4ECBXjzzTd58OCBTXV6e3tTtmzZWNN+/vln8uTJQ8mSJS3TvvrqK5555hly587NM888Y/dZ0ZCQEN555x3y589PyZIlWbx4cbxlVq5cyXPPPUfu3LmpUaMGv//+u2Ve9+7dadasGe+88w558+alQoUKfPPNN7Hme3h40KNHD86dO2d5r6x9xj/77DOKFy+Or68vH3zwQYa8ZUPEkSi7bKPsSrnscnNzo1OnTnh7ewNgMpmYPHkyRYsW5dlnn7Usp+zKmnRlKRPr1q0bhw8fZu7cudy/f58BAwbEmj9v3jwGDBjA2LFjKV++PDNnzqRJkybs27eP3Llzs3fvXpo2bcorr7zCiBEjOHjwIAMHDsTT05OOHTvaVMOECRP47rvvmDZtGnnz5mXu3Lm0bt2akydPYjAYEl3/wYMHNGnSBLPZzNdff83t27cZMmQIFy9ejPUlNWTIEAoWLMi3337LTz/9xPDhw2nUqBHly5dPdB9Tp04lODiYr7/+mj179jBt2jSAWFdHYi67ceNG3nzzTfz8/CzTjx07RosWLXjzzTcZM2YMW7Zs4a233rKEbFhYGM2bN8fHx4clS5Zw5coV+vXrh9Fo5OOPP060Rj8/Py5dukRgYCD79+/Hz8+PAwcO8PDhQ8v8xHTp0oVGjRqxZ88eevfuzdatWwHi3fKwbt067t+/z/jx47l16xZ9+vShevXq9OjRI9F9xHX69GkWLFjA4MGDMRojz8388ccf9OnThyFDhlCnTh02b95Mjx498PPzi9fQSsigQYNYt24dEydOxNvbO95ne/PmzXTu3JlBgwZRt25dli9fzquvvsrff//NU089BUTeMvjyyy+zdOlStmzZQq9evcifPz+NGzdmyJAh9OjRgw0bNvD111+zfPlygHi368yYMQN3d3dmzZrF/v37GT58OA0aNLCEuojYT9ml7IoprbPr66+/ZurUqdy+fZuNGzfi4uICKLuyMjWWMqmjR4+yadMm5s+fT4sWLQC4ffs2H374oWWZcePG0a1bN8tZpgoVKlCkSBF++OEHOnXqxOTJkylcuDBff/01zs7OPP/881y/fp3bt2/bXMfZs2cpXLgwrVu3xmAwUKFCBXbu3El4eLjlC+hxli9fztGjR9m1axelS5cGIHv27HTs2JF9+/ZRsWJFAAwGA8uXL8fJyYl69eoxf/58Dhw4YFPgRG93w4YNnDhx4rFf3j/99BObN28mb968saZPmDCBsmXLMnnyZABq167Njz/+yKJFi6hXrx5Lly7l7NmzbNq0ybLuvn37WLRokc2BYzAYOHLkCAcPHqR58+YcOHAAs9lMkSJF4tVjjY+PDz4+PgQHB1u2ac2JEyfYu3cvvr6+AKxdu5b9+/cnuv24TCYTPXr0wNfXl549e1qmR5+B7dq1K/nz56dmzZpUrlyZnDlz2rTd4OBg5s2bx/Dhw+nQoQMARqOR1q1bW5YZN24cTZo0YciQIQDUrFmT1atXs3z5cj766CMAvLy8+Pbbb/H09OSFF15gx44dzJw5k8aNG1O0aFGKFi3KwYMHLc8kWHP9+nX27t1Ljhw5aNiwIUuXLmX//v0KHJEkUnYpu+JK6+wqW7YsderUYcGCBcydO5cJEyYAyq6sTI2lTOrEiRMAVKlSxTKtVq1alv8PCgri3LlzzJw5M97tCSdPngQivxCrVauGs/Ojj8nw4cPtqqNTp06sWrWKqlWr4u/vT/Xq1WnWrJlNYQOwe/dufH19LaEA8Pzzz1vmRQdOt27dcHJyAsDJyYmcOXNazlylpOHDh1v9cj948CD79u3Dw8Mj1nQ3NzfL/IcPH1KsWLF464aFhSX6cLC3tzdPPvkkhw8f5tixY4wfP57+/ftjNBqpXLly0g/IildeecUSNgB58uRJ0ns5ceJEtm/fzs8//0z27Nkt0xs3bkyRIkWoVasW9erVo0qVKjRv3pz8+fPbtN3Tp08THh6e4GcbIt/vW7duxft9RP+7gMgHiT09PS2v/fz8WLNmjV3H2KFDB3LkyGF5ndT3SkQiKbuUXUmVUtlVo0YNatSoQaNGjWjZsiWvv/46NWrUUHZlYWosZVImkwnA8iUc9/+jDRs2jJdffjnWtHz58gFgNpvj3W5w7tw5rl27xnPPPWdTHbVr12bfvn389ttv7Nixgw8++ICJEyeyZcsW3N3dE13fWg3Rt3PFvL+2ePHiNtWTXDG/5OJq2rQpgwYNijUtZiPB19fXckk8ppiB/jiVKlXi999/x9vbm6eeeorDhw/j6upK3bp1bazeNk8++WSyt7F582Y++eQThg0bRvXq1WPNy507N7t27eLXX39l+/btzJw5k2HDhrF582bLbQaPY+tnu3v37rz55puxpj3xxBOW/497f7bJZLJ8tmyVEu+ViDyi7Eodyq7Hi4iI4Ny5c7F+Hy+//DJOTk4cPXqUGjVqKLuyMHXwkElF/0PYvXu3ZdqOHTss/+/p6UnhwoW5efMmzzzzjOVn3rx5/PXXXwBUrFiRv//+m4iICMt6n3zyCX369LG5jkmTJnHy5Enat2/PtGnTWLFiBQcOHODvv/+2aX0/Pz8uXLgQ66zK5s2bLfOiWfvCsZebmxv3799P0rrlypXj/Pnzsd7LHTt2WAKmXLlyXL9+HV9fX8v827dvM23aNJsflI0+e1SqVCnc3Nzw9PRk69atdp+diw76hI41ue/l4cOH6dChA40aNeL999+PN3/NmjUsX76cpk2bMmbMGMvn0loYW1O8eHGMRmOsz/b27dtjLVOuXDmuXLkS6/exfv16Nm7caFlm//79BAYGWl7v2rWLEiVKxNqOu7v7Yz8T9gaUiDyesst+yq5IyXkvz58/T/ny5WN14nHy5EkiIiIoUqQIoOzKynRlKZN6+umn8ff3p3///pjNZu7du8cnn3wSa5lBgwbRr18/ChQoQNWqVVm7di1z586lXbt2APTt25cXXniBLl260KVLFw4dOsSKFSuYOHGizXWcPn2axYsXM2zYMHLlysW8efNwcnKyfPkkpnXr1kydOpW2bdsycuRIbt++zeDBg2nevDnPPPOM7W+IDapUqcKoUaNYs2YNnp6eHD58ONazNo8TEBBAtWrV6NWrF61bt+b48eMMGjTIcutHmzZtmDBhAu3atSMgIICHDx8yYMAAypUrZ/P4HH5+foSFhVl6FipVqhRnzpyhUqVKdh1nmTJl8PT05PPPP6dOnTocPHiQFi1a2HwrweM8fPiQDh06YDAY+N///seePXss84oWLUru3LkJDQ1l4MCBADz11FP8888/BAUF2XyG1cvLi7Zt2zJ+/Hhy5cqFl5dXvIdkBw4cSNOmTS0PrW7fvp2xY8eyYMECyzL379/nzTffpFevXvz+++9s376d77//PtZ2KlWqxK1bt/j6668pWbIk//77r+XhZhFJecou+ym7kp9dxYoVo1mzZrzxxhuMHDmS3LlzM2LECJ599ln8/f0BlF1ZmBpLmdh3331Hv3796N69O97e3vTv35/+/ftb5nfu3Jn79+8zffp0Ro0axVNPPcXSpUstl+ufffZZ1q1bx+DBg3n11VcpUqQI48ePt2u08zFjxvDRRx/Rr18/bt26RfHixfn2229t/nJxd3fnhx9+4MMPP+TNN9/E3d2dNm3axAvPlFC3bl0GDx5Mnz59uHPnDnXq1LE5cJ566ilWr17Nxx9/zMKFC/Hx8eGjjz6id+/eQOSZv7Vr19K/f386duxItmzZaNq0qV2jkVeqVAmj0RgrcE6dOmXp6tRWXl5efPPNNwwcOJDx48dTuHBhmjVrZtc2EnLw4EGOHDkCEG8U9tmzZ9OxY0dat27N5cuXmTRpEhcuXCB37twMHDiQN954w+b9fP755wwYMIABAwbg5OQU670GqFevHvPmzWPs2LFMnTqVYsWKMWvWLMsD49HLFClShLZt2+Lp6clnn30W77aekiVLMm3aNMaMGcOVK1coVaoU/fr1S8I7IyK2UnbZR9mVMr744guGDh3KgAEDMJvNlm7lo59TU3ZlXYaQkBB1rC4iWUr37t25efMmK1euTO9SREREbKLsSh+6HiciIiIiImKFriyJiIiIiIhYkW5Xlm7evMmOHTu4ceNGepUgIiJiF2WXiEjWki6NpeXLl1OxYkX69etHmTJlbOp2cevWrfj5+VGkSBGmTp2aBlWKiIg8ouwSEcl60ryxdOfOHQICAvj555/Zvn07U6dOZejQoY9d5/r167Ru3ZpWrVrx22+/sXTpUrZs2ZJGFYuISFan7BIRyZrSvLEUHBzM+PHjefrppwGoUKECd+7ceew6S5cupUCBAgwcOJCSJUsyaNAg5s2blwbVioiIKLtERLKqdO3g4eHDh7z77rsYjUbmzJmT4HLvvPMO2bJlY/LkyQBcvnyZV155hV27dlldPjQ0lNDQUMtrk8nErVu3yJ07NwaDIUWPQUQkqzObzQQHB+Pj45MlBj1UdomIZGz25Fa6DUq7b98+GjdujIuLC3v27HnsskFBQZQpU8by2svLi0uXLiW4/IQJExgzZkyK1SoiIok7duwYvr6+6V1GqlJ2iYhkHrbkVrpdWTKbzezdu5fBgwfj5eXFkiVLEly2U6dOVK9enf/9738AREREkCtXLu7evWt1+bhn5wIDA3nqqac4Pwy83FP2OESyvLPAuRg/kuUEmqFwEFy6dAlvb+/0LidVpUt2eYKXLiyJiKQYe3Ir3a4sGQwGnn32WebMmUOZMmW4ffs2OXPmtLpszpw5Y3XTGhQUhKura4LbdnNzw83NLd50L3c1lkRSnBvgAjgB+oMuS8sKt4qlS3YZ1FgSEUkNtuRWmt9cvnnzZgYPHmx57eTkFFnIY+4X9PPz459//rG83rdvHwULFky9IkVERGJQdomIZE1p3lh66qmn+Prrr/n666+5cOECH3/8MfXr18fb25vAwEAePnwYb50mTZqwfft2tmzZQnh4OFOmTKFBgwZpXbqIiGRRyi4RkawpzRtLPj4+zJ8/nxkzZvDcc89x//59vvrqKwCqVavGxo0b462TJ08exo4dS/PmzSlRogSHDh2if//+aV26iIhkUcouEZGsKV27DrfXyZMnOXr0KLVr18bLy8vm9QIDA/Hx8eHu2ISfWTJjINzlCSKcPdGDF5J2zDiFB+H88A4GMsw/xdjOxPmRLCfQDN6BkV1j2/PdnFUkO7u8En5mKcI9Gw/z5IUs8LyYOAizGafgIJzv3sFgzqC5JVmePbmVbh08JEWJEiUoUaJEim83zDUvl4u9y72cz4HRGTWWJO2YwRRO9tv/4nNmNq5h19O7IBFJYamVXcFly3Ph/SGYvbxRbknaMUNEONl3/4vPt7NxvaHckswtQzWWUoPJ4Mzppyfi9ERxCj7hjquTIkfSjhkIi4Dr2V/itEcpSv3XBaM5PL3LEhEHF+GejQvvDyF7seLkdXJSbkmaMQNhwPUXXuL0k6Uo1bsLxnDllmReWb6xFObmg8k9D4VzuZM94R5dRVJNNhdwyeXO2Qd5CHMrgPuDC+ldkog4uId58mL28iavkxPZ1FKSNJYNcHF352zuPITlK4D7JeWWZF5p3sGDwzEYAQNGhY2ko8jPnwEMTuldiohkBAYDYNAVJUk3kX9AGsBJuSWZmxpLIiIiIiIiVqixlImdOXcJQ4Eq6V2GiIiITc5cuoTBT7klIo5DjSVxWJu37aLYc83SuwwRERGbbN65i2JNlFsimYkaSyIiIiIiIlaosZRMERGQt9wzDBzlS1Bw2r6de/YfpUaTLuR4sg61mnbl4JGTNq23dccenq3/BtmL16bKy504cPiEZd7Pm3dQ1r8V2YvXplbTrpw886iHmwUrfqTYc83wKO5Po3a9uXnrjk37mzBzPj4VG+JV8nna9RhCaGgYAGazmc9mzKdo5ab4VGzIlC8XA3Dl2g0MBapQ7/UenL1wGUOBKhgKVOHKtRsARERE8PH42fhUbEix55ox69sVln2FhobRqdcwnihdj3xPv8T4afMs84KCQ2jdbRBeJZ+n4DONmD53mU31i4hkJhERkPeFZxg41ZegkDTOrSNHqdG5Czlq1aHWW105eNLG3Nq9h2fbvkH2mrWp0qETB07EyK3tOyj7Wiuy16xNrbe6cvJ8jNz64UeKNWmGR01/GvXqzc07d2za34Tv5uPzUkO8/J+n3aAhhIbFyK158ynauCk+LzVkyqKo3LpxA4NfFep178HZy5cx+FXB4FeFKzdi5Nas2fi81JBiTZoxa3mM3AoLo9PQYTxRpx756r/E+G9j5FZICK0HDMLL/3kKvtSI6UuUWyJpTY2lZDKbwdXFzMTZ+SnnXz7NGk2BQcE0bNebZi/7c3TbCqr7lad9z6GJrmcymWj59kBaNW3Aqb9XU7NKRT4cOdUyv9N7w+jarhnHtq2kfJkSfDRuFgDBIfd4q+9Ixn3Ui0N/LMPZ2YkJsxYkur8jx88wcPQMln4xht2/zOfE6fN8u3Q9AAtWbGDstG9Z8sVovv/6Uz4aN5s///6P/Hlzc/vob6z7bhKFffNz++hv3D76G/nz5gZg8pzFLFnzCz8smMy3U4Yx7LM5rN6wGYBvlqxj+679/LtxHuvnf84nn8/lyPEzAIyY8CVnzl/iwOYlLJk9mn7DJnHpigbTE5GsxZJb8/NTrmX5NGs0BQYH07BXb5rV9efoqhVUr1Ce9kNszK3+A2nVoAGn1q6m5jMV+XByjNz6eBhdWzTj2KqVlC9Rgo9mRuXWvXu8NWIk43r34tCKZTg7OTFhvg25dfoMA6fNYOnYMexeOJ8T58/z7dqo3PphA2O/+ZYlY0fz/Wef8tHM2fy55z/y587N7S2/sW7yJAoXyM/tLb9xe8tv5M8dlVuLFrPkp1/4Ycpkvh0xjGGz57D6980AfLN2Hdv37effBfNYP+VzPvlyLkdOnwFgxJwvOXPpEgeWLWHJ2NH0mzSJS9eVWyJpKcuPs5QiDBAebuTCZVcmzs7PwpW5af/6TYb0vYxnDlOq7HL9L3+SK6cXg3q/BcBH/bpStdLTNq2797eFeHvmYN/hEwQF3+PYqXOWednc3QkNe4i3Vw5mfzoIkymyfiejERcXZ0JDw8iXJydrv5uE2WxOdF/ubpGDV4WGPaSIbwH+3vCtZd68Zevp3qEFNZ6rCMArL9Zm7U9/ULvaszzh7UkOj+wYDUae8PaMtc05C1YxPKAbfhXLANC3eztmf7eSFo2eJ5u7GyaTiYcPw6lSqRx3j/+O0Rj5R8CQvl0Y4eLC1Rs3OXnmAk5OTpw8c4GCBfLa9L6JiGQm4RFGLlx1ZeL8/CzckJv2jW4ypOtlPD1SKbe2/kkuLy8GdYnKrbe7UvVpG3NryUK8c+Rg3/ETBIXc49i5GLnlFpVbOXIwe0ic3HJ2JjQsjHy5crJ2sp259fAhRXwK8Pd331rmzVu/nu6vtaDGM1G55V+btVv+oHalZ3nC05Mc2aNyyzNObn2/iuHvdMOvbFRuvdGO2StW0qLe82Rzc8NkNvEwPJwqT5fj7h8xcqtrF0b0cOHqzZucvHABJ6MTJ89foGBe5ZZIWtGVpRQWs9GUmleaLly6RrFCPpbXOZ/wok2LlxJdz2g0Mmn2InwrNaHnwE+5GxRMRMSjYFw8exSb/9qFT8VG1G72Nrv3HQEgWzZ3ls8Zx5wFq8j79Es0bNebU2cvJrq/YkUK8tXEIQwcPZ3cZRvQpvtgbty8A8DFK9eZOncpT5SuxxOl67Fqw2bOXbyS6DbPX7rKk0V9La+fLOrL+YtXAXjjtYa0alqfVzr2o0CFhgwcNd0SjnsOHOXZBu2p99q7bNr6D9mzucc6dhGRrChmoyk1rzRduHqNYgVj5JaXF21etjG3FizCt2ETeo77lLvBcXJrzCg279qFz0uNqN3lbXYficotd3eWjx/HnO9Xkbf+SzTs2ZtTF2zIrYIF+WroEAZOnU7ueg1oM3AwN6Ju37t47TpTlyzliTr1eKJOPVb9vplzV2zIratXedI3Rm4V8uX81ajcatiQVg3q80qffhR4sSEDp8bIrSNHebZte+p1f5dN//xDdnd3IkzKLZG0pMZSKoluNE36Ij+VXyyHDSez7FLYNz+nz1+yvA4OuUf5um0sz/UkZPO2Xcyat5LDW5ex8+fv6NquuWVeSMh9Qu7d55dlM7h15Ff8qz1Ll36fAHDz1h1yPuHJtnVzubr/J/LlyUm/jyclWueFS1epWK4Uu36ez7ld67h9N5BPPp8LQCGffIz88B3+27SQ/zYtZO+vCxk7uKdlXaPRYPUsYBHfArEaaifPXKCIbwEAjpw4wzsdX+PUP2vYumYOC1ZuYNWPmwHo0vcT+nZry9ld65g/fSSuLrqwKiISLbrRNGlBfiq/kQq5VSA/py/FyK179yjfqo3luZ6EbN65i1krVnJ45TJ2LvyOri1i5Nb9+4Tcv88vs2Zwa/Ov+Fd6li4jonLrzh1yenmy7Zu5XN30E/ly5aTfRBty6+pVKpYqxa5F8zn34zpuBwbyyZdRuZU/HyN7vMN/Sxby35KF7F2ykLHvxcgtQwK5VaAApy7GyK3zFyhSICq3zpzhnddf49S6NWydO4cFGzawKuoWvS4jPqHvG205++M65n+i3BJJD2ospRJnZxOFCobxQY+r7PrlUORg6ymoSYNa3L4TxJgp33Dh0lVGfT6XCJPJ8lxPQoJD7gFwNzCYbf/s5f3hn1u+2E1mE0069GPBih+5cesORqPRcjvDjVt3qN+qJxt/+4vA4BCMBiMmU+JJeujYaRq90Ydt/+wl5N59DAaDZZudWzdh8eqfCQq5x737D+geMIYZ3yy3rPtkUV8uXb3Orr2HOXH6PHv2HwWge4dXGT7hS/bsP8qWv3Yx5csl9Oj8OgCLV/3EW31HcujoKcLDIzCbzZb9Bd+7x4PQMM6cu0SP/mO5ev2WTbdkiIhkBc7OJgrlD+ODjlfZtSgVcqt2LW4HBjFm7jdcuHqVUV/NJSLCZHmuJyHB96JyKziYbf/t5f1JMXLLZKJJn34s+OFHbty5E5VNUbl15w71e/Rk47ao3DIaMdnwnX/o1GkavdeHbf/tJeR+nNx6pQmLN0bl1oMHdB81hhnLYuRWIV8u3bjOrkOHOXHuPHuOROXWa68y/Isv2XPkKFt27WLK4iX0aBmVWxt/4q3hIzl06hThEXFy6/49HoSFcebSJXqMHsvVm8otkbSmUxQpzNnZRIF84XR4/SaD+6TeM0tenjnYuHgq/xs4nrFTv6VyxTKs+vozDImkW8MXatDsZX/8XuxI8SIF6da+BQNHT+fq9Zvkz5ubBdNHMvTT2XQLGEPJYoWYNX4gAE+VLMbEYX14d8B4rly/yTPlSjF30keJ1vnS89V5p+OrtOo2kDuBQdR8riJD+nYBoP3rjbh05QZNOvQlMCiEFg3rMrJ/D8u6hQrm59OhvXm53XuEhYUzdvD/qFThKfp0a8udu0E0eqM3bq6uDA/oRvOGdQEY0Ksz3QNGU6vZ25jNZto0f5HXmtQDYOqoAD4YPpnPZs7nrbZNKV+mBHsOHKVe7eeS9DsQEckMnJ1NFMgdTofGNxncJfWeWfLKkYON06fyv7HjGfvNt1QuW4ZVE23IrZo1aFbXH783OlLctyDdXm3BwGnTuXrzJvlz52bBqJEMnTmbbqPGULJwIWYNisqtYsWY2K8P744dz5WbN3mmVCnmDrMht2pU553XX6VV/4HcCQ6iZsWKDHk7KrcaN+LS9Rs06dOXwOAQWtSry8geMXIrf34+7d2bl3u9R9jDcMb2+h+VyjxFn3ZtuRMURKP3euPm4srwd7rR/Pmo3HqzM91HjabWW1G59dKLvPZCVG59GMAHn0/ms+/m81azppQvWYI9R49Sr4pySyStGEJCQjL9KYrAwEB8fHy4Oxa83GPPe5CtKKefnU1x3zy4u9i/7fBwKFq5ItduOqdJI0kypwcP4fTFGxT/rwfu98+mdzn2ORPnR7KcQDN4B8Lly5fx8vJK73IyDUt2eYFXnPbEg8JFOT1hNsXz5sHdzitA4eFQtElFrt12TpNGkmROD8xw+voNigf0wP18BsstyfLsyS1dWUomgwEehhsIePeqGkkiIuLwLLnV8aoaSSIiiVBjKZmcnODawb3pXYaIiIhNnJzg2q/KLRERW6iDBxERERERESvUWBIREREREbFCjSUREREREREr1FgSERERERGxQo0lERERERERK9RYEhERERERsUKNpUzszLlLGApUSe8y0sWbvYcz/LM5Dr9NERF55MylSxj8smhuDRvO8NkpnFupsE2RrEbjLEmmNHPcQIxGO4e1FxERSSczByq3RByRGkuSKWXP7p7eJYiIiNgsezbllogj0m14yRRhiiDvty8ycMc0gsJC0nTfe/YfpUaTLuR4sg61mnbl4JGTNq23dccenq3/BtmL16bKy504cPiEZd7Pm3dQ1r8V2YvXplbTrpw8c8Eyb8GKHyn2XDM8ivvTqF1vbt66k+i+un0wiqHjZ1ler/v5DyrWa2d5/d2yHyhV4zXylGvA4DEzMJvNlnmGAlU4eOQk73w4hlxl6nM3MBgAs9nMB8M+J0+5BuR86gX6fDQh1npg/Za58PBwBo+ZQYEKL+NTsSHDPv3CMi8iIoKPx8/Gp2JDij3XjFnfrkj02BJbL/o2yOs3btO62yCKVH7Fpm2KiKSmCHMEeTe+yMBD0wgKT+PcOnKUGp27kKNWHWq91ZWDJ23Mrd17eLbtG2SvWZsqHTpx4ESM3Nq+g7KvtSJ7zdrUeqsrJ8/HyK0ffqRYk2Z41PSnUa/e3LxzJ9F9dftkFENnxsitLX9QsXWM3Fr/A6Wav0aeFxoweFqc3PKrwsGTJ3ln1BhyPV+fu0ExcmvS5+R5oQE5675An8+s5JaVW+bCw8MZPG0GBV58GZ+XGjJsVpzcmjUbn5caUqxJM2YttyO3Elgv+jbI67dv03rAIIo0Vm6JqLGUTGbMuBpdmLh3IeWWtk6zRlNgUDAN2/Wm2cv+HN22gup+5Wnfc2ii65lMJlq+PZBWTRtw6u/V1KxSkQ9HTrXM7/TeMLq2a8axbSspX6YEH42LDIzgkHu81Xck4z7qxaE/luHs7MSEWQsS3V/LV+qz4dftltcbf9vO601eACIbbd0CRjN55PtsXjmb+St+ZOHKDbHWf/uD0Xjl8GDV15/iEXW16Kfft/PN0vX8unwmf675klUbNvPz5h2J1jJh1gJWbdjMT0um8cOCycz+7ntWb9gMwOQ5i1my5hd+WDCZb6cMY9hncyzzHseW9V7r2p/KFcuweNboRLcnIpLazGYzrgYXJp5cSLnfWqdZoykwOJiGvXrTrK4/R1etoHqF8rQfYmNu9R9IqwYNOLV2NTWfqciHk2Pk1sfD6NqiGcdWraR8iRJ8FNXQCb53j7dGjGRc714cWrEMZycnJsy3Ibfq12fDthi59dd2Xq8flVu799Dtk9FMDnifzXNmM//HH1n4Y5zcGjkaLw8PVk38FI+oq0U//bWdb9au59fZM/nz6y9Z9ftmft5uQ27NX8Cq3zfz04xp/DBlMrNXfs/q3zcDMHnRYpb89As/TJnMtyOGMWz2HMu8x7Flvdc+6E/lsmVYPEa5JaLb8FJIuDmCCyHXmLh3IQuPb6R9qYYM8euCp6tHquxv/S9/kiunF4N6vwXAR/26UrXS0zatu/e3hXh75mDf4RMEBd/j2KlzlnnZ3N0JDXuIt1cOZn86CJPJBICT0YiLizOhoWHky5OTtd9NindWzJoXalfhzIWhXL1+k/x5c/PT5h2s/uYzAOYt+4FXGz1PkxdrA9Dh9Uas/WkrHVo2tqxfsVxJPhvWJ9Y2s7m7YTKZCA19SPlnSnDm37UYDInf5/314nV80v8dnnm6NAArvhpHrie8AJizYBXDA7rhV7EMAH27t2P2dytp0ej5x27TlvUa16/JgPc6J1qfiEhaCjdHcOHBNSaeXMjCCxtpX6ghQ0p3wdM5lXJr65/k8vJiUJeo3Hq7K1WftjG3lizEO0cO9h0/QVDIPY6di5FbblG5lSMHs4fEyS1nZ0LDwsiXKydrJ9uYW1WqcObyUK7evEn+3Ln5afsOVk+Kyq31P/Bqvedp4h+VW40bsfaPrXRoEiO3SpXks34J5FbYQ8qXLcGZ9Tbm1pp1fPLuOzxTOiq3Ph1HLu+o3Pp+FcPf6YZf2aj8eaMds1espEW95x+7TVvWa1y7JgPeVG6JgK4spbiYjabUvNJ04dI1ihXysbzO+YQXbVq8lOh6RqORSbMX4VupCT0HfsrdoGAiIkyW+Ytnj2LzX7vwqdiI2s3eZve+IwBky+bO8jnjmLNgFXmffomG7Xpz6uzFRPfn4uLMKw1q89PvOzh+6hxOTkbKly0JwMXL11i9cQtPlK7HE6XrMeWrJZy7eCXW+r27tom3zbo1KzO491u81XcEecq9yDsfjuHevQeJ1nLh8lWKFSloee1fvRJPlykBwPlLV3myqK9l3pNFfTl/8Wqi27Rlvd5d2ya6HRGR9BKz0ZSaV5ouXL1GsYIxcsvLizYv25hbCxbh27AJPcd9yt3gOLk1ZhSbd+3C56VG1O7yNruPROWWuzvLx49jzveryFv/JRr27M2pCzbmln9tftq+g+PnonKrZFRuXbvG6s1beKJOPZ6oU48pi5dw7nKc3GpnJbcqV2Zwl7d4a/gI8rzwIu+MHsO9Bzbk1rWrFCsYI7f8KvF0iajcunqVJ31j5E8hX85ftSG3bFivdzvllki0dGksrV+/nqeffhovLy/q1q3Lkagvtsdp2bIlHh4elp8mTZqkQaVJF91omrRvEZVXdLTpbJY9Cvvm5/T5S5bXwSH3KF+3DVeu3Xjsepu37WLWvJUc3rqMnT9/R9d2zS3zQkLuE3LvPr8sm8GtI7/iX+1ZuvT7BICbt+6Q8wlPtq2by9X9P5EvT076fTzJplpff+UFNvz2V6xb8AAKFcxHj06v8d+mhfy3aSF7f13EvKnDYq3rkT1bvO2dOH2epi/5c/CPZRzYvIQduw4w+7uVidZRuGB+Tp979J59Ov07Ph4/G4AivgViNf5OnrlAEd8CiW7TlvU8POIfg4hkPJk9u6IbTZNOLqLyllTIrQL5OX0pRm7du0f5Vm24ciOR3Nq5i1krVnJ45TJ2LvyOri1i5Nb9+4Tcv88vs2Zwa/Ov+Fd6li4jonLrzh1yenmy7Zu5XN30E/ly5aTfRBtz64UX2LDtr8hb8F6IkVv589Hj9df4b8lC/luykL1LFjFvZJzcymYlt86dp2kdfw6uWMaB5UvYsf8As1fYkFv5Y79nn377HR/PisqtAgU4dTFG/py/QJECNuSWDetZOwaRrCrNG0unTp2iR48ejBw5kuPHj1OkSBF69uyZ6Hp79uzhn3/+4eLFi1y8eJFly5alQbVJ52x0ppBHPj6o2J5dLefbdLndHk0a1OL2nSDGTPmGC5euMurzuUSYTOTPm/ux6wWH3APgbmAw2/7Zy/vDP7cEoslsokmHfixY8SM3bt3BaDRabme4cesO9Vv1ZONvfxEYHILRYMRksi1IX6pbnb93H2D9pj9jNZY6tWrCmo1/cOX6TcIjIhgydhZDxs56zJYi/fbnv7zedQB79h8lLOwhBoPBplreatuUERO/ZO/BY+zae5hpXy+lbOniAHTv8CrDJ3zJnv1H2fLXLqZ8uYQenV9PdJtJXU/Sjsls4LPAD9l0vz6hZtdkr5uc7UnGlRWyy9ngTCH3fHxQoj276qZCbtWuxe3AIMbM/YYLV68y6qu5RESYyJ87kdy6F5VbwcFs+28v70+KkVsmE0369GPBDz9y486dqGyKyq07d6jfoycbt0XlltGIycYG4Es1qvP3gQOs/+NPy/NKAJ2aNGHNlj+4cuMm4eERDJkxiyEzbMitf//l9Q8HsOfIUcIePsSAjbnVrCkjvviSvceOsevQYaYtXUrZ4lG59dqrDP/iS/YcOcqWXbuYsngJPVrakFtJXE/SjnLLsaT5M0tHjx5l+PDhvP565D/Mt99+mxYtWjx2nYsXL2I2m3naxnub05Oz0ZkC2XLRoVQjBvu9lWrPLHl55mDj4qn8b+B4xk79lsoVy7Dq688SDbeGL9Sg2cv++L3YkeJFCtKtfQsGjp5ueaZowfSRDP10Nt0CxlCyWCFmjR8IwFMlizFxWB/eHTCeK9dv8ky5Usyd9JFNtbq5uVK9cgX++ncflZ8pa5nuX70SwwO60bHXMK5cu0l9/yrM+Wxwott7q20zdu8/ystt3+P+g1Beer4a776Z+Bd9wLsdCAwK4aU27+HkZKTnW61o9+rLAPTp1pY7d4No9EZv3FxdGR7QjeYN6ya6zaSuJ2nJgBMRbA+ryf6HFangsg9/9624GcKStG4ttz+TsT3JqDJzdjkbnCnglosOhRoxuPRbqfbMkleOHGycPpX/jR3P2G++pXLZMqyaaENu1axBs7r++L3RkeK+Ben2agsGTptueaZowaiRDJ05m26jxlCycCFmDYrKrWLFmNivD++OHc+Vmzd5plQp5g6zMbdcXaleoQJ/7d1H5XIxcsuvEsPf6UbHocO4cvMm9atWYc5HNuRWs2bsPnKUl3u+x/3QUF6qXo13W9mQWx07EBgcwkv/ew8no5GerVvRrmFUbrVry52gIBq91xs3F1eGv9ON5s/bkFtJXE/SknLLkRhCQkJS9jq7nb766itmz57Nzp07E1xmzZo19O7dG1dXV+7cuUPjxo2ZPHkyOXPmtGkfgYGB+Pj4cHcseMUZxuBBtqKcfnY2xX3z4O5if/3hpnCKLmjGtQe306SRJJnTg4dw+uINiv/XA/f7Z9O7HPucifPjYExmI5OD+hJkjnwo2kgEOQzBNoWFtXU9CCYUd8Jws3t7mVWgGbwD4fLly3h5eaV3OWkiTbPLC7zitCceFC7K6QmzKZ43D+52XgAKN4VT9JdmXAu7nSaNJMmcHpjh9PUbFA/ogfv5DJZbDk65lfrsya107eAhLCyMKVOm0K1bt8cud/z4cSpVqsSaNWvYunUrZ8+eZfjw4QkuHxoaSmBgoOUnKCgohSt/xICBh6aHBFRsz6E2yxhbvZcaSiIOzIQTgWZvtofVZGZQT7tuSzDhRBDehOEaa1pStycZU0bPLoPBwEPzQwJKtOfQC8sYW66XGkoiDky5lb7StbE0YsQIPDw86NKly2OXCwgIYPXq1ZQrV44yZcrwySefsHr16gSXnzBhAj4+Ppaf0lFdbqYGJ6MT1978RY0kkQzmUVjUYE7wO9j3LHv8U/nJ255kJBk9u5wMTlxr+IsaSSIZjHIrfaTbOEu//vorc+fO5ffff8fFxb7737y9vblx4wahoaG4ubnFmx8QEMB7771neR0UFJSqDSYRyXji3oZg37PsZuIGT/K2JxmFsktE0otyK32kS2Pp9OnTdOnShcmTJ1O2bNlEl2/fvj19+vShatWqAOzevZv8+fNbDRsANze3BOeJSNaWnHu1de931qbsEpH0oNxKX2neWLp//z4tW7akadOmvPLKKwQHBwPg4eFBUFAQ2bJli3e2rnz58gwYMIBPP/2UGzduMHLkSLp3757WpYtIBpbcsIlet5bbn8wK7km42Vlhk4Uou0QkrSm3HEOaN5Y2bdrEkSNHOHLkCN98841l+qFDh2jYsCGffvopTZs2jbVOQEAA586do2nTpuTNm5du3boREBCQ1qWLSIZkJgInarj+lYRwiL+uyWxIxvYko1J2iUjaUW45knTvOjwtpGbX4SIpISt0HW4yG5gYFEAll92p9mUdbjIyOmgovsYLtPZYipcxOMX3IfFlxa7D00JqdR0ukhKyQtfhyq3My57cSrcOHkQkq0nOIHu278OAmYumQkwOep+CxosKHxERSSLllqRz1+GSus6cu4ShQJX0LiNR3y5Zx/OvvpOi2xz+2Rze7D08zdYT26XN+A4GzBgt4fNVUFcCTTlSeB8iktLOXLqEwS8D5NbadTzfLYVza/Yc3hw2PM3WE9spt7I2XVmSdPfGaw1p2bR+im5z4HudMZnsv8M0qeuJ/WKGT2qesTNj0Bk7EUlRbzRqSMsGKZxbbyUxt5K4nthPuZU16cpSMpkizHxW7hc2jTpMaHB4epeTIbm6upDDI3uKbtPd3Y3s2d0TXzCF1pOkS5tB8R6dsZsS1A+TKTX2IZIxmCLMfPbCL2yaepjQEOVWUri6uJAjewrnlpsb2bMlIbeSuJ4knXIra1FjKbnMZpxcDGyffZqZ/lvStNG0Z/9RajTpQo4n61CraVcOHjlp03pbd+zh2fpvkL14baq83IkDh09Y5v28eQdl/VuRvXhtajXtyskzFyzzFqz4kWLPNcOjuD+N2vXm5q07ie6r2wejGDp+luX1up//oGK9drGWSeg2vGLPNWPTH38zeMwMClR4mb0Hj1nmLV71EwUqvEypGq/x4Ygp5Hv6pVjzrd1O92bv4QwdP4ueA8eT48k6lPNvzeFjp2Mtk9BteJPnLKJI5VfIXbYB7/Yfx8OHj37Hwz79gvzlX+aJ0vXo0nckERERib4v8oiRCLwMd6nhup3uOb5IpUHxzBgw4Ws8Tx/PzzHqm0+ysujcmn+amS23pGmjac+Ro9To3IUctepQ662uHDxpY27t3sOzbd8ge83aVOnQiQMnYuTW9h2Ufa0V2WvWptZbXTl5PkZu/fAjxZo0w6OmP4169ebmnTuJ7qvbJ6MYOjNGbm35g4qt4+RWArfhFWvSjE1//83gaTMo8OLL7D0WI7c2/kSBF1+mVPPX+PDzKeSr/1Ks+dZup3tz2HCGzpxFz7HjyVGrDuVeb83hU3FyK4Hb8CYvXESRxq+Qu14D3h0TJ7dmfUH+Bi/zRJ16dBmu3LKXcitr0VufEgxgCjcTePlBmjWaAoOCadiuN81e9ufothVU9ytP+55DE13PZDLR8u2BtGragFN/r6ZmlYp8OHKqZX6n94bRtV0zjm1bSfkyJfhoXGRgBIfc462+Ixn3US8O/bEMZ2cnJsxakOj+Wr5Snw2/bre83vjbdl5v8oLNxzl0/BdcunKdxbNGU6JYIQDu3A2ie8AYlswew7AP3mb+ih/Z9/sinipRNNHtffHdKnJ4ZOfA5iXky5OTsVO/TXSdJat/Zty0eSyc8Ql/rfuKLdt3M+ObZQCs/3krn89ZzMbFU9n183y2bN/NinW/2nx8WdmjsPmL/3nOoEG2X1Ohp6FHYdPXcxJve36tWxlEopgizARefZBmjabA4GAa9upNs7r+HF21guoVytN+iI251X8grRo04NTa1dR8piIfTo6RWx8Po2uLZhxbtZLyJUrwUVRDJ/jePd4aMZJxvXtxaMUynJ2cmDDfhtyqX58N22Lk1l/beb2+Hbk18wsu3bjO4jGjKVEoKreCgug+agxLxo5hWPe3mf/jj+xbuoinitqQWytXkSN7dg4sX0K+XDkZ+823ia6z5KefGfftPBaO+oS/vvmKLbt2M2NZVG79sZXPFy1m4/Sp7Fo4ny27d7Nik3LLFsqtrEnPLKWwmI2m/SsvUeH1gvj3LYVbjpR9q9f/8ie5cnoxqPdbAHzUrytVKz1t07p7f1uIt2cO9h0+QVDwPY6dOmeZl83dndCwh3h75WD2p4MwRV33dTIacXFxJjQ0jHx5crL2u0mYbbju/ELtKpy5MJSr12+SP29uftq8g9XffGbzcXp7efDt1OGxph07eQ7PHNl5vlZlQkPD6NhrGGZz5C10iSlUMB/jh74HwBuvvcziVT8nus7Xi9fSr/sb+FevBMCCGSMJD488C1ev1nOc3bmWiAgT23ftw8nJKdb7KfGlzcjhZgyYKWi8QGuPZQoakceI2Wjav+ESFRoVxL9rKdw8Uji3tv5JLi8vBnWJyq23u1L1aRtza8lCvHPkYN/xEwSF3OPYuRi55RaVWzlyMHtInNxydiY0LIx8uXKydrKNuVWlCmcuD+XqzZvkz52bn7bvYPUkO3Irhwffjhgea9qxs+fwzJ6d55+rTGhYGB2HRuWWmw25lT8f4/tE5VbDl1n8kw25tWYt/dq/gb9fVG6NGkl41NWjelWe4+wPUbm1bx9ORqdY76fEp9zK2nRlKZVYGk1fnGbOi1tt+oK2x4VL1yhWyMfyOucTXrRp8VKi6xmNRibNXoRvpSb0HPgpd4OCiYh4dCPs4tmj2PzXLnwqNqJ2s7fZve8IANmyubN8zjjmLFhF3qdfomG73pw6ezHR/bm4OPNKg9r89PsOjp86h5OTkfJlS9p8nO91aRNvWtHCBbh9N4iTZy6wY9d+cuX0Jn/eXDZt7/malS3/7+riYtPv5cKlaxQr/Oi99qtYhqp+kQF/49YdXn3rQ8r4t2TWtyvJns0t1vspj5jMEGT2xMd4ia45vqRBtl9x4SGfBX4Yq2chk9lg0zTrzJgxxDojl8MQYtO6tu7D9lrSh6PXJ47L0mhacJo5b6RCbl29RrGCMXLLy4s2L9uYWwsW4duwCT3Hfcrd4Di5NWYUm3ftwuelRtTu8ja7j0Tllrs7y8ePY873q8hb/yUa9uzNqQs25pZ/bX7avoPj56Jyq6QdudXWSm75FOB2UBAnz19gx/795PL2Jn9uG3OrchJy6+o1ivnEyK2yZahaPiq3bt/h1Q8+pMzrLZm1YiXZ3ZVbCVFupQ1Hr0+NpVRidDbgVdCdGj2K0/0XfwwpfENrYd/8nD5/yfI6OOQe5eu24cq1G49db/O2Xcyat5LDW5ex8+fv6NquuWVeSMh9Qu7d55dlM7h15Ff8qz1Ll36fAHDz1h1yPuHJtnVzubr/J/LlyUm/jyfZVOvrr7zAht/+svsWPACP7NniTXN3c6NMyaI8XbcNjdv3Zda4ARhtvJnXK4eHXfuH+O/1wpUbePv9UQAM++wL8ufNxfWDv/DjoimUfjLxWyqyKqMBPA1BXDYVZG5wN8uXYvQYFo+6Y3WzMs3acvG/UJ2NJoZ5j4hz24LBpnVTfrn04uj1iaMyOhvwyu9OjY7F6b4oFXKrQH5OX4qRW/fuUb5VG67cSCS3du5i1oqVHF65jJ0Lv6Nrixi5df8+Iffv88usGdza/Cv+lZ6ly4io3Lpzh5xenmz7Zi5XN/1Evlw56TfRxtx64QU2bPsr8ha8F+zMrWxWcsvVjTLFivJ0qzY0fq8vswbZkVseScit/LHf64U/buDtkVG59cUX5M+Vi+u//sKP06ZQ2oZbAbMq5VZacez61FhKYZZG0rvF+d8fdWkwpGyK34IH0KRBLW7fCWLMlG+4cOkqoz6fS4TJRP68uR+7XnDIPQDuBgaz7Z+9vD/8c8tZKpPZRJMO/Viw4kdu3LqD0Wi03M5w49Yd6rfqycbf/iIwOASjwWhzV6Uv1a3O37sPsH7Tn3Y3lqxZuf43fAvkY88vCzi/ez2tm7+Y7G0+zlttmzJ5zmL+/Ps/jhw/w2cz51OimC8AwSH3iYgwceXaTabPXcb3P/6e4mdjM5uYXa/OCvofoWa32NOC3+VB3GlWlrP3C9XWdVN6ufTi6PWJ44jZSPrfiro0eK9sit+CB9Ckdi1uBwYxZu43XLh6lVFfzSUiwkT+3Ink1r2o3AoOZtt/e3l/UozcMplo0qcfC374kRt37kRlU1Ru3blD/R492bgtKreMRkw2fj+/VKM6fx84wPo//rTreaWErPz1N3zz5mPPogWc37Ce1i+lcm41b8rkRYv5c89/HDl9hs++m0+JQlG5de8+ESYTV27cZPqSZXz/m3IrMcqttOGo9emZpZRgjgybHPncIp9R6pPyzyjF5eWZg42Lp/K/geMZO/VbKlcsw6qvP0v0TGDDF2rQ7GV//F7sSPEiBenWvgUDR0+3PFO0YPpIhn46m24BYyhZrBCzxg8E4KmSxZg4rA/vDhjPles3eaZcKeZO+simWt3cXKleuQJ//buPys+UTfaxv1i3Kh+MmEzNpl25GxhMrpxejBvSi7fbt0j2tq1p2+IlLl+9Qbt3h/DgQRjtX29IwLsdARj6flc69PyYMrVb8mKdarRt8SJ7DhxNlToyGxNOBOENmGNPM1uZZmW5pI51kdC6LoSn6HKpc0+7/Ry9Pkk/RmcDOXK7UaFxQfy7pPwzSnF55cjBxulT+d/Y8Yz95lsqly3Dqok25FbNGjSr64/fGx0p7luQbq+2YOC06ZZnihaMGsnQmbPpNmoMJQsXYtagqNwqVoyJ/frw7tjxXLl5k2dKlWLuMBtzy9WV6hUq8NfefVQulwK5Vb0qH3w+mZpvdeVucDC5vL0Y914v3n61RbK3bU3bl1/i8vUbtBs8hAehYbRv1JCAjlG51a0rHT76mDKvt+TFatVo+9KL7Dmq3LKFcittOFp9hpCQkEx/OiEwMBAfHx/ujgWvOEMRPMhWlNPPzqa4bx7cXezftinCzMSKm6j0RqE0aSQJdOz1MUUL+fBe19YYMPDd8h9YvPpndv08P71LS7IHD+H0xRsU/68H7vfPpnc59jkT5ycBJrORyUF9CTJ7pXgJRsJ5wniXXjmmxevCNbH9Rq/7P48ZTAlOueWs1ZIWbD3elKwv0AzegXD58mW8vFL+95tVWbLLC7zi/K4eFC7K6QmzKZ43D+52/h5NEWYmvrSJSi0KpUkjSaDjRx9T1MeH99q2xmAw8N36H1i88Wd2LcrAuWWG09dvUDygB+7nM1hu2Ui5lTYcPbf0DZlMRicDHx5M3cvpElv71xoyYNR0Jn2xEICKZUsxcVjf9C1KksEMxP32s21a3B6K7PkSjbduSi+XDoHzOI5en6Qdo5OBD39VbqWl9o0aMmDqdCYtjMqtkqWY+H7f9C1KkkG5lRYcpT41liTDafhCTRq+UDO9y5BkMhKBB8GE4k4YbpZpOQzB3De78zDGtISWS8ql+YTWNZmNKbqco3D0+kSygoa1atKwlnIro1NupQ1Hq0+NJRFJUzG/BGu5/cms4J6Em51jTNvGrOD/ERFrWvzlUipsUnu59OLo9YmIZBTKrbThqPWpsSQiacRMBE7UcP3L8iVoMhuSPC05+02b5dKLo9cnIpJRKLfShmPXpw4eshXh9LOzKeabl2xJ6OBBJCXcfwhnLl6P6uAhg42kfgabOnhICyazgYlBAVRy2f3YL1xbl3MkjlyzOnhIHY/t4KFQEU5PnE2xvHnJ5mDPGUjWcN8MZ65fp/gHPXC/kMFyy4Eot9KHOniwg0vYTYgI414YaixJurkXBkSE4RL2+MEZJTGPBrZ7fHejti7nSDJizZJanIKDICKcMCD+EKgiqe8eQFgYLjeVW8mj3HJ0Wb6x5BQRwhOX13LNpR3wBNld4/dlIpJazEQ2lK7dvMMTl9fiFHEvvUvKFGwdo8HRxnKwRUasWVKe8907ZN+zk+v1XsTF3V0jzEuaMRPZULp25w5PbFyL033lVkpQbjmuLN9YAihw/hsArj1sBk6uqLkkaccMEWE8cXmt5XMoKSezDNBnTUasWVKOwWzG55tZnC5ekrO586DckrRjhrAwnti4lgKLlFspTbnleNRYAgyY8Tn/NfkuLeGhq0JH0pIZl7AbuqKUyh59QdfgcHg5/ucxw6bl0muAPntkxJolZbjeuE6p3l0Iy1cAnJzSuxzJKsxmXG7e0BWlVKbcchxqLMXgFHEPp4z2cL2IJCqzDNBnTUasWVKOMTwc90sX0rsMEUlhyi3HocaSiGRamWWAPmsyYs0iIvJ4yi3Ho8aSiGQ6mWWAPmsyYs0iIvJ4yi3HpcaSiGQimWWAPmsyYs0iIvJ4yi1Hp95GRcThmcwGPgv8kE336xNqdk1wmq3rGg1mPvT6jAbZfo1xi4Nt20sLttYsIiKOSbmVeXJLV5ZEJAOIP7BdLbdtVqb9adNyjj/gnyPVIiIi9lNuZZbcMoSEhJjTu4jUFhgYiI+PD3fHgpd7elcjksmcifOTCkxmI5OD+hJk9gIi73/2MATzwOzOQ9weTSOYUNwJiznNynIJPTwbdx/pdY+1I9Vii0AzeAfC5cuX8fLySu9yMg1LdnmBl4P3FiUisSm3Mk9u6cqSiGQ4JpwIMnsTOZZ8jGlYmWZluYw24J8j1SIiIvZTbmXc3NIzSyKSgVk73W7btJiD4s0JfgdzAtfYbV0uLThSLSIikhTKrYxGV5ZEJAMzEz9QbJuWEQf8c6RaREQkKZRbGY0aSyKS4UR/+d634d5va8tltAH/HKkWERGxn3Ir4+aWGksikmHE/PKt5baNWcH/I8LsHGPan8wK7kl4rGnxl8soA/45Ui0iImI/5VbGz610eWZp/fr1PP3003h5eVG3bl2OHDmS6Dpbt27Fz8+PIkWKMHXq1DSoUkRsYTIZ+GzDh2y69PixJJIz5kTMge3+5zkjasyGUCvTwmxezrZ9pOyXfHKO14WHDjOeRlal7BLJHJKTUcot2483s+RWmncdfurUKerUqcOUKVOoXbs2AQEBXLp0iV9//TXBda5fv07FihXp3bs3rVq1onPnzowZM4a6devatE91HS6SekynjEz+si8hDzzinRULMSc2LfKMWsxpmeVMlDXRXasm5XiTs25qywpdh6drdqnrcJEUZe37VLllnXIrHRpLGzZs4OLFi7z99tsAbNmyhRYtWnD79u0E15k+fTpz585l9+7dGAwG1q9fz/fff8/XX39t0z7VWBJJPdGNpaAHiYwlYeP4Eo72hZqSkjMOhSOPYZEVGkvpml1qLImkKJvHQFJuKbdIh2eWGjVqFOv18ePHKVGixGPX2b9/P3Xr1sUQ1YVG5cqVGTZsWILLh4aGEhoaankdFBSUjIpFxB4JjiVh4/gSmWVcBlsk53iz2nuV3pRdIpmXcst2WTG30nWcpbCwMKZMmUK3bt0eu1xQUBBFixa1vPby8uLSpUsJLj9hwgR8fHwsP6VLl06xmkXEVqk/lkRmkZzjzWrvlSNQdolkVsotW2Wl3ErX3vBGjBiBh4cHXbp0eexyzs7OuLm5WV67u7tz7969BJcPCAjgvffes7wOCgpS6IikuRQcSyKT34KUnOPNau+VI1B2iWRWyi1bZaXcSrfG0q+//srcuXP5/fffcXFxeeyyOXPm5MaNG5bXQUFBuLom3KuGm5tbrIASkbTzuDEibB1fIqNcmk+u5BxvVnuvHIWySyTzUW7ZLivmVro0lk6fPk2XLl2YPHkyZcuWTXR5Pz8/li9fbnm9b98+ChYsmJolioidEh9LwrbxJTLSF2hSZcWwyQyUXSKZi3LLdlk5t9K8sXT//n1atmxJ06ZNeeWVVwgODgbAw8ODoKAgsmXLFu9sXZMmTXj//ffZsmULtWrVYsqUKTRo0CCtSxcRq8xEmJyokfcv/EMjvwRNZoNlvIXoL0Zbp2Vu5mQcb3LWleRSdolkJvG/T5VbCVFupXnX4evWraNt27bxph86dIiGDRvy6aef0rRp03jz58yZQ//+/fH29iZ79uxs3ryZ/Pnz27RPdR0ukorOxPkhchC7iUEBVHLZbfmCDDcZGR00FF/jBVp7LMXLGGx1c9bWdSSOXl96yApdh6drdqnrcJFUp9zKWhx6nKXkOHnyJEePHqV27dp2BbIaSyKp6AxWGkvxB6Kr4foXE4M/xIwBA2YKGi9aDR9HHsQOHL++9JAVGkvJkezsUmNJJNUpt7IWhx5nKTlKlCiR6LgWIuI4Yo6psO9hxaiRKQyYMXDRVIjJQe8nHD4OPh6Do9cnjkPZJZJxKLckrgzVWBKRjMna4H4JhU8Ow7146zryl7uj1yciIvZTbkm0dB2UVkSyGuuD+5kxctFUiClB/TCZrK/p6IPYOXp9IiKSFMqtrE5XlkQkDVkf3C/yXvALtPZYhjGBZzMcfRA7R69PRESSQrmV1amxJCKpzkgEHoZggsyePAqd2GETfe+3yWyMt64jP5Dq6PWJiIj9lFsSTY0lEUk1Mb+QH/UqZD1sHreuI36ZO3p9IiJiP+WWxKXGkoikgvgD0YWbjJgx4Gs8/9iwcfxB7By9PhERsZ9yS6xTY0lEbBb60MjY/kMxFbrAu12X4vNEMOHhRkavGYpvtgu0do7sStVoMPOh12ex1jUazGQ33KOY89nHflFbW9caa4MF2jrwXnKWs7U+DQIoIpL+Qs1GxgYOxeR9gXeNS/ExBVvND+WWcishaiyJiM3MJgNmgxnjhULMHvk+Zt+LdKu/AgNmLt4vxGQSHn8CDDgRkYJdlUYOEhizC9eW2ZfbuA9ba0lOzSl9vCIiYq/IG+jMGO8WYrbhfcxeF+nmtCJefii3UuN4Mwc1lkTEbgYMGMwGzBcK8dV3fSJfp9ugfY/2OzW4LwZMNu8jpZezRuNZiIikP0tu3S3EVwbl1uMot2JTY0lEkswSPg4xaJ8h6gzio75PE9qHC+E21WLrcgofEZGMQbml3LKXBqUVkWQzONSgffFrsXUfKb1cSq8rIiIpQ7lle3VZPbd0ZUlEki2yW1VHGbQv/gCC8faRwJpJXs6OmjUIoNitCOCU3kWIZFBmYJ+1ycotW2X13FJjSUSSzIwZs8GMwWyINTV9Bu2LvpnBhCnqqy2hfdhaS0rWrPEtJMmKAC7pXYRIBhVOrMaScku5ZS+7G0vHjh1jwoQJXLhwAXOc63AbNmxIscJExHFFh4250AW6vbCSb+b1iTovlh6D9j0KuZbZV/B1yNuEmD1s2oettShsMr4MnV3+gHt6FyGSQYUCP4LZHCO3Oqzkm7HKrYQot2Kzu7H0xhtvkDdvXlq3bo2Li051iWQ0JpOBiR8HUKnabvxf3Iqbu+1fggZj5Nk4U+HzvNtlWeQ4SyeiBu3Ldp7WzpFhE2GAvN2h6wEY8g94PoSUHxTPHG+wQJPZYOM+bK0lOTVrEEBHouwSybhSPLfCo3LrifO09lmG1yXlVvLXzbwMISEhdj2mlT9/fnbu3EnhwoVTq6YUFxgYiI+PD3fHgpfOzkkWZ4owMnlkX0KCPcjhFUwFv312h08sZ+L8AOEGKPo2XMsGBe5B+8Mxw0cym0AzeAfC5cuX8fLySu9yrFJ2iWRcKZ5bEC+7lFtZiz25ZXdveO3bt+err75KcnEi4gAMYDI5EXjHm+2bazJzfE82ratP6APXlNuHGcKd4IInTKwM5TrDwFoQpJP6kg6UXSIZnHJL0olNt+GNHTvW8v85c+Zk7ty5/Prrr/j7+8dqjQ0aNCjlKxSRVBUzfPbvrpgyZ+ziiBk+C8vqjJ2kDWWXSOak3JK0ZFNj6cyZM7Fev/zyywDcunWLW7dupXhRIpL2LOGzpQaH95Wj1+BpKd49aHT4TKoM35eCo99aG11CJGUou0QyN+WWpAWbGktffPFFatchIunMaIyIvBe88j78G6TOOArOEVAgBDocgcH/KHAkdSm7RDI35ZakBbt7w9u5cyeVK1fGEOMT+e+//7J+/XpGjBiRosWJSOqLGzYpeRtDtLhho9sYJK0pu0QyD+WWpCW7G0v16tXj4sWLse73LlCgALNnz1bgiGQU5jQIG4PCRhyHskskg1NuSTqxu7FkNptjnZkD2L9/v8N2FysicRjMREQ4UaPeX6l2Rs4APDRCwC6FjTgGZZdIBqbcknRkc2MpR44cGAwGDAYDBQsWjDXPw8ODGTNmpHhxIpLyzAYzn/b/jK4uUM0N3BJYLsIMBYKgqwsMcQdPO27UdjLDtTiPi0QYoEC8Af/Sh8lsYGJQAJVcdmvgvUxO2SWS8Sm3lFvpyebG0qFDhzCbzZQvX57t27dbzsYZjUYKFCiAs7PdF6lEJB2YAVdgYhgsfAjtEwgVW5eza78mR+mG1YATEWwPq8n+hxWp4LJP4ZNJKbtEMj7lFii30o/Ng9IWKVKEokWL4urqStGiRSlSpAhFihShUKFCChuRDCgcuGCODJVyQTDwPgSZk76cTRxswD8TTgSavdkeVpOZQT3ZdL8+oeYUHOBQ0p2ySyTzUG4pt9KDzY2laJs3b8bNLaELoCKS0aRL+KDwkbSl7BLJPJRbyq20ZHdjqVmzZty9e5erV6/Gmj5v3jz8/f3Ztm1bihUnImknOlQmhUHlYDAnECjxlkvufmMM+Fe5ffK3lxyPwqcGc4LfSfA9kIxH2SWS+SQ5t5L53a7cylrsbiy98cYbVKtWjRIlSlC0aFFWr14NwMcff0zLli0ZOnRoStcoImnAGShkgA9cYVcOEhzcL95yyd1vBBQKhA92wa6F6Tvgn5EIvAx3qeG6ne45vkiVAQ4lfSi7RDKfJOdWMr/blVtZi903bP/www+MGTOG1157jb///puWLVtSt25dbt++zRtvvMHo0aNTo04RSSXOQAEDdHCBwTEehA0327ZckvfrQGNZGIkghyFYD8xmYsoukcxDuaXcSkt2N5YCAwMpXLgwrq6uFCpUiIiICK5fv463tzcQOZaFiDg+W0MkRcPGwQb8U9hkHcoukYxPuaXcSg92N5bGjRvH66+/jqenJ3fu3KF06dIMGTIEs9nM7NmzKVGihE3buXnzJv7+/mzYsIGiRYsmunzLli3ZsGGD5fXzzz/PDz/8YG/5IlmeAXgIBLg+PkRsXc6u/TrMgH9mInCihutfCpssIiWyS7klkj6UW6DcSj92P7PUunVrjh49ypIlSzh48CAbN26kadOm/P3335w9e5ZPPvkk0W3cuHGDli1bcvbsWZv3u2fPHv755x8uXrzIxYsXWbZsmb2li2QaEWbIG5i0nn2Cw+G6GSaEwX/hj9+emcQfXA0DjM9C9TpwKXvkNJPZwGeBH8bqncdgMvDhZx9Sf1N9XMPSt8ceo8HMh16f0SDbrwqcLCK52aXcEkkeh8otExifgOql4FJUJ5nKLUmIISQkJM3vPWjSpAmNGzemf//+HDp0KNEzdBcvXsTf359Tp04laX+BgYH4+Phwdyx4uSdpEyIOJdwMRYPgmjnyVgN7Bt678RDy3nv02hn4JRu0fxB7ewPcoHxw4vt4cAY8ngATYDRDlSuwYq2RZVf7EmL2sNwuUMvtT2YF94w1TWfHModAM3gHwuXLly2DvmY2aZ1boOySzMWhcssEHoHKrazMntyyu7G0fft2RowYwYULF+Ld433w4EGbtnH69GmKFy+Oh4eHTaGzZs0aevfujaurK3fu3KFx48ZMnjyZnDlz2rQ/BY5kNtGhcynqn2D0/dm2hE/c0LHGGcgP3AWCY0yztg9LYyl6n2ZwijDy4aS+uN+L/AIyEoEHwYTiThhulmkKn8whIzSWkptdaZ1boOySzMWhciu6saTcyrLsyS27n1nq2rUr1apV48MPP8TVNWmXJIsXL27X8sePH6dSpUqMGTMGo9FIjx49GD58OFOmTLG6fGhoKKGhoZbXQUFBSapTJKOIOfDewof2nbFLaHsXbdxHvPH4DGA2QJgTRP99Z8KJILyJeXNEzAH19j+sqPCRVJXc7Ert3AJll2Qtyi3JKOy+suTr68tff/1l08OtibH1DF1cW7dupUOHDgneOz569GjGjBkTb7rOzklmEfcMXVwuQDEjHLUynoQtZ+hsEb2PvTchxxMxztABxggjfSf3xSvI9qsMRsJ5wniXXjmmaZyIDCYjXFlKqexKrdwCZZdkbg6VW9khR5ByKyuzJ7fs7uDhvffeY/To0Tx8mH5dgnh7e3Pjxo1YZ+BiCggI4PLly5afY8eOpXGFIukjpQfes2kfdq0dPyU1oJ6khfTOrsRyC5RdkjWlS27ZtQ/lVlZn9214p0+f5ueff6Zs2bJUq1YNT09Py7zZs2enaHHR2rdvT58+fahatSoAu3fvJn/+/Li5uVld3s3NLcF5IplRig+8R8L3fsfdx4O4K5vBYAbXiEeTdO+3pLe0zi57cwuUXZK1pGtuxW3/KLfkMexuLBUtWpTu3bunRi0EBgaSLVs2XFxi301avnx5BgwYwKeffsqNGzcYOXJkqtUgkpGkRNhY61Wogwv0j+pV6IHZxn2YI3sVqnoFlq+FZfchJEawRPcqFG52VthImkut7FJuidhHuSUZjd2NpcGDB6dGHQBUq1aNTz/9lKZNm8aaHhAQwLlz52jatCl58+alW7duBAQEpFodImkhwgwFgqBrEh5qtXXgvTATuAdBVSN87wEFjY8ebHUGfssO/i6Ry102x17ufkTkveU+wK8eUNrp0bgWMWs2EnmTQvVbsHIFFLwHJnP8wfNMZkOqD6hnMhuYGBRAJZfdCjSJJbWyS7klWUmmy60QWPkHFDyq3JKE2d3Bw8OHD5k8eTI//PADFy5cYO3atfTo0YN58+bZ3VtQWlH3q+KIkjPmhK0emMAjKGosCaBKjFBJrJYPXKFAcOx1l2WHGiFxar4KnmeA6J90ZDIbmRzUV2NipLGM0MGDsksk+TJNbrmD51ke5daZlKvfXsqt9JGqHTz07duX1atX06lTJ4KDg8mePTtVq1alV69eSS5YJCuL2bVpuaCkjW5uCxPwtwkKB0H1ILhkenwtlUIi14m5brHgyFHUY9XsBQN9IMju69SpJ2b3rjODesYakV2yJmWXSMrJ8LkVBAPdIcjuv4JTj3LLcSWp6/Bt27ZRrFgxfH192bFjByaTiapVq3L16tXUqjNZdHZOHFFC3ajaM1BfYmKeoYsr5hm7fIbHd+maGGcTFHgA7Q/AkH/AM506y4w+Qxdkjn2WSA/lpq6McGVJ2SWSfJkqt8xQ4CG0Pw1DflFuZTWpemXJ19eXP//8M9a0kydPpsi4SyLy6OzXpDCoHAzmVDhbBzHOugWByVoq2SHcCBeyw6TKULm9tY5W09ejM3Y1mBP8Tqq9p+K4lF0iqSdD5pYBLrjCpFLKLXk8u2+cGTVqFG3btuXrr7/m3r17DB48mG3btjFnzpzUqE8ky4nbU1Bqjd9gJPLB2JUeYExul61RV5Y6HIDB/9g79lLqi3uGTmNiZD3KLpHUkyFzK+rKUofTMPgX5ZYkzO7G0ksvvcSuXbtYvnw5FStWxNfXl08++YRixYqlQnkiWUdKjzmRkJhhE/3AbHgCt1RciDPdEDXvYczlIqDDdRj8L3ieSJ2ak0q3MUg0ZZdIysuwuWWADvdh8DHwPBVjpgNQbjmeJD2SXbx4cfr375/StYhkSekZNo+r5f04vQpVNcLSOL0KdXCBwVfB8zKR92E4CIWNWKPsEkkZGT633MHzFtYfjEonyi3H5UD9V4lkLbaOOZEclrEkEgkba7WEmeKvG2FO/ZqTJ/44GSIikjKUW6lBueXo1FgSSYbkDNDnZIBrcTpguR8B2YMjB9TbnCNyQD1rA/TZOs3JALkNUNf5UW0J1Wwm9gOurkYweSdesyMxGsx86PVZepchIuKwlFuORbnl+GzqOrxcuXIYbHiy7ODBgylSVEpT96uSWlJ6gL7gCPAMfvTaB9iYHSrdiz3I3uJsUDIk9rRF2aBUSOKD8Q1wg/LBiU+z+TjO4BAD+0n6cdSuw5VdIvEpt6KcQdmVhdmTWzY1lrZu3WrTjv39/W2rMI0pcCS1xB1zIrljTcQNnYQYiX+rtYH4XZ8aASfiP9h6xwzBMablB+4Se5rNx3EGBU4W56iNJWWXSHzKrShnUHZlYfbklk234TlqkIg4mpgjhC98mDID9Flj7ZlUa2c9THGWja6PONMuEn9aWhyHSGpSdokkTrkl8nh2D0orIolLqwH6UltmOQ4REXm8zPJ9n1mOQxyHGksiqcAZKGSAD1xhV47UG6AvtWWW4xARkcfLLN/3meU4xHGoNzyRFJSWY0+kxb3fqX0cIiKSvpRbIo+nK0siKSD6TFaAKxzyhLHZUuaL2gfYm/3RP1QjkeNHnPCwbdrpHJDXELu+fTnAK860/Z7xp6XkcYiIiGNRbonYRleWRJIhpQfoc4r6b9zxKuIOsmfrNGuD8dk6TUREMh/lloh91FgSSYbkDHZnbZA9VyPkMUAnF/CJOt2W0CB7cQfts7YcxB+0z1rN1qYlZ+BCERFxTMotEfvoNjyRdGIGXIns5rRcEAy8H9nvf9xpQVZ68rG2bnKWs7U+W9cVEZHMR7klWZEaSyLpLObYEBWCIoMn5rTHfeGn9HKJ1afwERER5ZZkJWosiTiI6EH2guNMU/iIiIgjUm5JVqDGkkgGYOsgeym9nE3r2ncoIiKSBTh0bim4xA5qLIlkALYOspfSy9m0rn2HIiIiWYBD55aCS+xgU2945cqVw2DDJ+vgwYPJLkgkq7JnkL1wc/x1k7OcrfVpwD/JSJRdIqlLuSVZgU2NpS+++CK16xDJsmJ+mfd3g/LB8MBs2xe8rUGgsJGsSNklkjqUW5KV2NRY8vf3T+06RJLF0cdWsFafyQyXzVDVCN8/ZjA+a2wdVDA5gw+m9MCFImlN2SWOzpGzS7klEkmD0kqmEHNshYUPob2DBY+1+ga4gY8BdpmgWvCjmm0ZLNDWQQWTM/hgctYVEZHEOXJ2KbdEIqVYBw9hYWEptSmRJHP0bkJj1lcx2L6xKUQk5Sm7xBE4cg4otySrs/vK0uXLl/n00085fvw4ERERlulHjx7l1KlTKVqcSFLF/CJ3tLN18Kg+a9MctWaRjEzZJRmBI+eAckuyKruvLHXt2pVr166RLVs2smXLxmuvvcaxY8fo1q1batQnkiwZcWyFjFiziKNTdklGktFyIKPVK2IPuxtLu3btYtKkSfTp04fAwEC6devGzJkz+eWXX1KjPpFkyYhjK2TEmkUcnbJLMpKMlgMZrV4Re9h9G56Pjw+///47zZs35/Dhw9y/f5+nn35a41SIQ3H0bkOj67tjTnxsChFJPmWXZASOnAPKLcmq7G4sjRgxgi5dulC/fn1eeeUVqlatCkD16tVTvDgRezn6F3dyxqYQkaRTdokjc+TsUm5JVmd3Y6l58+acOHECLy8vpk+fztKlSwkJCeGNN95IjfpEbOLoYytYq8/WsSlEJPmUXeKIHDm7lFsikZLUdXjOnDlxcnLCxcWFDh068M477+Dp6WnXNm7evEm5cuU4e/asTctv3boVPz8/ihQpwtSpU5NStmRi0WMrjM1m/5d3mAmMd6F6EFwyJX+5CDPkDYzdlaq1+qxNs7autWkiYr/kZpdyS1JaUrNLuSWSduxuLE2aNImHDx/GmrZ582YaNmxo8zZu3LhBy5YtbQ6c69ev07p1a1q1asVvv/3G0qVL2bJli111iyTEROQZtL9NUDgo4VCxdbmYA/nZO/6EtXUDzUnfnohESm52KbfEkSi3RNKO3Y2lYcOGcf/+/VjTypQpw86dO23eRufOnWnZsqXNyy9dupQCBQowcOBASpYsyaBBg5g3b57N64vYykTioWLrcskZtC/muhWCNAigSHIlN7uUW+KolFsiqcvmZ5b+/PNPAMxmM9u3b8fDw8Py+pdffqFkyZI273T69OkUL16c/v3727T8/v37qVu3LoaovigrV67MsGHDElw+NDSU0NBQy+ugoCCbaxOB2KFSxQjfe0AuG5fLF+dWiuQM2hcOXLQyTYMAitgmpbIrtXMLlF2SPMotkdRhc2Ope/fuABgMBvr06YPRGHlRymg0UqJECb766iubd1q8eHG7igwKCqJMmTKW115eXly6dCnB5SdMmMCYMWPs2oeINdGhUiwI7njYtlxwDuvLxBy07/twOJrMsShSensimVFKZVdq5xYouyRlKLdEUpbNjaVDhw4BkCNHDv755x+8vLxSrai4nJ2dcXNzs7x2d3fn3r17CS4fEBDAe++9Z3kdFBRE6dKlU7VGyZyMQFUjrPR4/D2rsZZL4Is/btewyQ2IlN6eSGaUXtllb26BsktShnJLJGXZ3XV4gwYNcHFxSY1aEpQzZ05u3LhheR0UFISrq2uCy7u5ucUKKRF7xQyRglFp88DK/d3WlguPcz92csbPcAbyA3fRIIAiyZHW2WVvboGyS5JHuSWSOuxuLK1evdry/2FhYbi4uGA2my23NqQGPz8/li9fbnm9b98+ChYsmGr7k6zLWogkdbnkho0GARRJOWmdXcotSSvKLZHUZXdKBAUF0atXL4oVK0aePHk4cOAApUqVYs+ePckuJjAwMF7XrgBNmjRh+/btbNmyhfDwcKZMmUKDBg2SvT8RiPxHYAaqG+G8J2z3tB4kti4XcyC/Q572jZ9hbV0vQ9K3JyKRUiu7lFuSHpRbImnH7itLPXr04N69e3z55Zd06tQJb29vevToQb9+/di8eXOyiqlWrRqffvopTZs2jTU9T548jB07lubNm+Pt7U327NmZOXNmsvYlEs3VCCbvlFsuetC+pLC2bnK2JyKRUiu7lFuSHpRbImnHEBISYleP9wULFuTff//F19cXX19fduzYgdFoxM/Pj6tXr6ZWnQCcPHmSo0ePUrt2bbse0g0MDMTHx4e7Y8HLPRULFMmKzsT5kSwn0AzegXD58uU07fzHHumVXUnNLVB2iaSqMyi7sjB7csvu2/BKly7NwoULgciuWA0GA3///Tdly5ZNWrV2KFGiBI0bN3bYMBYREceUXtml3BIRydjsvg1v4sSJvPbaa8yZM4egoCA6derEuXPnYj3IKiIi4kiUXSIikhR2N5YqV67Mvn37+PHHH7l8+TK+vr40bNgQb28bbooVERFJB8ouERFJCrsbSwDe3t60adOGmzdvkidPHgwaVUxERBycsktEROxl9zNLV69epU2bNuTKlYsnn3ySXLly0alTJ65du5Ya9YmIiCSbsktERJLC7sZSjx49ANiyZQunTp3i119/5cGDB5bpIiIijkbZJSIiSWH3bXh///03O3bsoEiRIgDkzZuXzz77jBo1aqR4cSIiIilB2SUiIklh95Ulf39/Vq5cGWvasmXLqF+/fooVJSIikpKUXSIikhR2X1m6cuUKQ4cO5YsvvsDX15fz589z+fJlqlatSqNGjQDYsGFDihcqIiKSVMouERFJCrsbS926daNbt26pUYuIiEiqUHaJiEhS2N1Y6tChQ2rUISIikmqUXSIikhR2P7MkIiIiIiKSFaixJCIiIiIiYoUaSyIiIiIiIlaosSQiIiIiImKFGksiIiIiIiJWqLEkIiIiIiJihRpLIiIiIiIiVqixJCIiIiIiYoUaSyIiIiIiIlaosSQiIiIiImKFGksiIiIiIiJWqLEkIiIiIiJihRpLIiIiIiIiVqixlMFEmIzk/egaA9eNIehBjvQuR0REJFHKLhHJqNRYymDMZgOuTmFM3BxAufGHFDwiIuLwlF0iklGpsZQRGcyEm1y4cKewgkdERDIGZZeIZEBqLGVwCh4REclolF0iklGosZRJRAfPpC0fUHniLszm9K5IRETk8ZRdIuLo1FjKJJyNYRR64hwfPD+RXR9UxmBI74pEREQeT9klIo7OOb0LkORxNoZRwOsKHSovZHCDMXi6B6d3SSIiIo+l7BKRjEKNpYzIbFDQiOMoBpyJ8f+S9UQAB9K7CHF4yi4RyYDSpbF08OBBevTowalTp+jcuTOjR4/GkMi195YtW7JhwwbL6+eff54ffvjBvh0XAbInoWAHYogw8xAXAlpMZHDLMXhmCyYiwkjet67RtcFXDGkVOU0kTT0f9d8zPGo4SdbxkCzRWFJ2JZ2ySxxOMR5l1pl0rEPSx0PgR9sWNYSEhKTp45ShoaFUqlSJBg0a0LdvXwICAmjRogWdOnV67HolSpRg7dq1+Pr6AuDi4oKHh4dN+wwMDMTHx4e7W8ErE3a2Ex7uRNHGZ7l2Kx8F8lyhfaMFDHl7DJ4eCh5JY2fSuwBJD4H3wLs9XL58GS8vr/QuJ1Uou1Keskscxpn0LkDSmj25leaNpXXr1vHuu+9y7NgxsmfPzr59+3j//ffZtGlTgutcvHgRf39/Tp06laR9WgLnLGTGHA8Pd6JoxTNculwIAGfnhxTId4X2rRYw5IMxeHoqeEQk9QQGgnfRzN1YUnalPGWXiKQXe3IrzXvD279/P1WqVCF79sh7CipUqMCRI0ceu87OnTuJiIigVKlS5M2bl86dO3P79u0Elw8NDSUwMNDyExQUlKLH4OjCw124cKkwE2cEUK76IQYOH0NQUCY8LSkikkaUXalP2SUijijNG0uBgYEUK1bM8tpgMODk5PTYADl+/DiVKlVizZo1bN26lbNnzzJ8+PAEl58wYQI+Pj6Wn9KlS6fgEWQc0cEzaeYHVH5e41eIiCSVsivtKLtExJGkeWPJ2dkZV1fXWNPc3Ny4f/9+gusEBASwevVqypUrR5kyZfjkk09YvXr1Y5e/fPmy5efYsWMpVX6G4uwcRqGC5/ig50R2bdb4FSIiSaXsSjvKLhFxJGneG17OnDk5dOhQrGnBwcG4uLjYvA1vb29u3LhBaGgobm5u8ea7ublZnZ5VODuHUSDfFTq0Xsjg93Xft4hIcim7Up+yS0QcUZpfWapcuTL//vuv5fXZs2cJDQ0lV65cCa7Tvn17/vnnH8vr3bt3kz9//iwdKvGYDZazcQG9JnJox9OMHTZYYSMikgKUXalE2SUiDi7NG0u1a9fm7t27LFy4EICJEydSr149nJycCAwM5OHDh/HWKV++PAMGDODff/9lw4YNjBw5ku7du6d16Q7LYDDz8KGLgkZEJJUou1KesktEMoJ0eWZp+vTp9OnTh2LFirF69WpGjhwJQLVq1di4cWO8dQICAihTpgxNmzalf//+dOvWjYCAgLQuPcVERBjJW/Jaoj393L/viiGniYJlLnDsRInHrmvGkOhDsLbuV0REYlN2KbtEJGtK83GWol26dIndu3dTrVo18ubNm6r7crSxKsLDnSha4SzXbuR77JgSwcHZ8CwcYnntk/8Sm1a/wIuv/hZr3QF9x1O+xsFEt2frfkVE7JEVxlmKpuxSdolIxmdPbqVbYyktOWTg2DAQ36PAie4KKPJXZTRGYDI5x1r3zl1vgkO8Hrs9DQAoIqkhKzWW0pKyS9klIqlDjaU4HD1wosUNAIMhIk7gRDPbNC3u9rJlu2/TfhU8ImIPNZZSh7JL2SUiqUONpTgySuBEc3EJo1jhM+za/CxeRawFjn2it3dwRzmKPZP4fo/ufErjWoiIzdRYSh3KLmWXiKQOe3IrzTt4kITZPhCftfZt/Gm2bk8DAIqISFIpu0QkM0vzQWklvoQG4gsOzhZnSWv3fYdRIN/VOPd9W99eeLiTTfsVERFJjLJLRLICNZbSS9RAfLZ94UcGjU+Bi2xaVT+qR6G8lnX794nsUehBqHvi27NrvyIiIjEou0Qki1FjKR3EHIjvcV/4Tk4RQGTQbF73PKVLniQiwhhvXWvTkrNfERGRuJRdIpIVqbGUDiIijNy4lYfftz7Pe92n4ukZzP37rmQv+ACf/JfYvL4upUuexNU1nDy5b9CpzXx88l8FwMnJxLUT+eNt05aB/RJaV0REJDHKLhHJitTBQzowmYwYDGb+3lWdwuXPU73BX1y46AvA5asFearKcQqWucDR4yVxdQlj4owAylU/lODo5WazwablREREkkrZJSJZkboOTwcPHrji4RtiedAVzBgMEZjNTtgyiJ8G7BMRR6Kuw1OHskvZJSKpQ12HZzgGzOa4d0QaAAMm06NegMLDXbhwqXCiZ+FsXU5ERCTplF0ikvnpylI6iH92Lmk0YJ+IOAJdWUodyi5ll4ikDl1ZyrA0YJ+IiGQ0yi4RybzUG55DiHnf96NpYG0QPw3YJyIijkDZJSKZn64spRsDYMZoDKf6c9s5+k/pqOlmwIxPgYsc3F6GAvmuWs6yBfSayKEdTzN22OD4IRI1YF+iy4mIiCSZsktEshY1lpIoIsJI3pLXEn0ANSzMGWOuCKo3+ItLlwsAcO+ea1QXrBH8uOxltv9SC2enkKg1zEwY+R6XDhcm1xPXuHSlIOHhzowf/gFjhw3GZDJhyGnCJe8Dtm6vDoDJZODyVR8qP7uLvzdVSzBobK1ZREQyJ2WXiIh91FhKIlvHh7A2LsWVawWituFEw5abyOF7l4NHyketYSDg42kYckawdPVrlmntuy/DkDOc79c3BSA83JU6jf/CJe8D/vq7Gj75L7Prv+eo1uAfjWkhIiJWKbtEROyj3vCSyNbxIayNSwERQPxxKSIldZqZ6LavxrQQkbSk3vBSh7JL2SUiqUO94aUD28eHMBC/Xw0DjwIkqdMeTdeYFiIiYgtll4jI4+nKUhLFPdMVV/T4EHv/rECOQskflyI5NKaFiKQmXVlKHcouZZeIpA5dWUpHto8PYdu4FMmZpjEtRETEFsouERHrNM5SCklofIgHD1zjLBnzvu+Y0+Kyd5qZ6NsZNKaFiIjYQtklIvJ4urKUHDaPDxF7XIqD28tGbwAw4+ERxLpFDWJNAzOTx74dZ5qJr6e3izXN2TmM39f6U7DAZY1pISIiiVN2iYjYTI2lJDIYzDx86JLol7bRaMJsNlD9ue2cP1CY7b/UokihCwB4eASxa/OzBF/w5rlKu6PWMLNwTmvMt5144/UVUdNMfD+/Kebbzrz2yjog8szaHz/W5OF1d/xrbrepFltrFhGRzEnZJSJiH92Gl0ROTiauncgfa1pEhJECT12ha4evLN2ZurqGY7oV+xaCHDkeYL4du52aK1cwBoOZqn5/83ztPwHw9r4XNe0fqlXeZZkWd11rtdhas4iIZB3KLhER++jKUgpKzsB51gYAvHipYLxp0SOpi4iIpARll4hIwtRYSmkGczLGgIh80NVkcubvXdUpWfkkJpMh1jQFj4iIpDhll4iIVWospaLkDZxniBrfwhBvmoJHRERSi7JLROQRNZbSQHTwTJr5AZWf34XZrmGArQ0c8Sh4ilU8i8mUQoWKiIhEUXaJiKixlCaSN3Ce9fEportyPbOvKEb9FkVEJIUpu0RE1BteqkrewHlmjMYITCYjj87QRU6r6vcPK797nYI+V1KhahERycqUXSIij6ixlNKiBs5LWtBEDwAYGSqLvmxHycqnMJkUNCIikoqUXSIiVqXLRfCDBw/i7++Pr68vgwcPxmzDjdBbt27Fz8+PIkWKMHXq1DSo0n7JGTjP2gCAvgUvxZumsBERSR/KrviUXSKS2aV5Yyk0NJRWrVpRqVIltm7dypEjR5g/f/5j17l+/TqtW7emVatW/PbbbyxdupQtW7akUcW2ix44Lymji0cPABgzVKxNExGRtKfssk7ZJSKZXZo3ln7++WcCAwMZN24cTz75JMOHD+e777577DpLly6lQIECDBw4kJIlSzJo0CDmzZuXRhWLiEhWp+wSEcma0ryxtH//fqpUqUL27NkBqFChAkeOHEl0nbp162KI6oqncuXK7N27N9VrFRERAWWXiEhWleYdPAQGBlKsWDHLa4PBgJOTE7dv3yZnzpxW1wkKCqJMmTKW115eXly6dCnBfYSGhhIaGhprfRERkaRSdomIZE1pfmXJ2dkZV1fXWNPc3Ny4f//+Y9dxc3OzvHZ3d+fevXsJLj9hwgR8fHwsP6VLl05+4SIikmUpu0REsqY0byzlzJmTGzduxJoWHByMi4uLzesEBQXFC62YAgICuHz5suXn2LFjyS9cRESyLGWXiEjWlOaNpcqVK/Pvv/9aXp89e5bQ0FBy5cqV4Dp+fn78888/ltf79u2jYMGCCS7v5uaGl5eX5cfT0zNlihcRkSxJ2SUikjWleWOpdu3a3L17l4ULFwIwceJE6tWrh5OTE4GBgTx8+DDeOk2aNGH79u1s2bKF8PBwpkyZQoMGDdK6dBERyaKUXSIiWVO6PLM0ffp0+vTpQ7FixVi9ejUjR44EoFq1amzcuDHeOnny5GHs2LE0b96cEiVKcOjQIfr375/WpYuISBal7BIRyZoMISEhiQ9BngouXbrE7t27qVatGnnz5rVpnZMnT3L06FFq166Nl5eXzfsKDAzEx8eHu2fBjtVERMQGgYHgXRQuX75s13dzRqTsEhHJ+OzJrTTvOjxawYIFH3vvtjUlSpSgRIkSqVSRiIjI4ym7RESyljS/DU9ERERERCQjUGNJRERERETECjWWRERERERErFBjSURERERExAo1lkRERERERKxQY0lERERERMQKNZZERERERESsUGNJRERERETECjWWRERERERErFBjSURERERExAo1lkRERERERKxQY0lERERERMQKNZZERERERESsUGNJRERERETECjWWRERERERErFBjSURERERExAo1lkRERERERKxQY0lERERERMQKNZZERERERESsUGNJRERERETECjWWRERERERErFBjSURERERExAo1lkRERERERKxQY0lERERERMQKNZZERERERESsUGNJRERERETECuf0LiAtmM1mAAKD0rkQEZFMKPq7Nfq7VlKGsktEJHXYk1tZorEUHBwMQOHy6VyIiEgmFhwcjLe3d3qXkWkou0REUpctuWUICQnJ9KcCTSYTly9fJkeOHBgMhlTZR1BQEKVLl+bYsWN4enqmyj7Sgo7Dseg4HIuOwzqz2UxwcDA+Pj4Yjbq7O6Uou2yn43AcmeEYQMfhaNIzt7LElSWj0Yivr2+a7MvT0xMvL6802Vdq0nE4Fh2HY9FxxKcrSilP2WU/HYfjyAzHADoOR5MeuaVTgCIiIiIiIlaosSQiIiIiImKFGkspxM3NjcGDB+Pm5pbepSSLjsOx6Dgci45DMpvM8lnQcTiOzHAMoONwNOl5HFmigwcRERERERF76cqSiIiIiIiIFWosiYiIiIiIWKHGkmRKN2/eZMeOHdy4cSO9SxEREbGJskvE8aixlEJu3rxJuXLlOHv2bHqXkmTr16/n6aefxsvLi7p163LkyJH0LilJli9fTsWKFenXrx9lypRh+fLl6V1SsjRv3pz58+endxlJ9sEHH+Dh4WH5qVChQnqXlCxDhw6lZcuW6V1GksyfPz/W7yL6JyN/viTplFuORdnlOJRbjsMRckuNpRRw48YNWrZsmaED59SpU/To0YORI0dy/PhxihQpQs+ePdO7LLvduXOHgIAAfv75Z7Zv387UqVMZOnRoepeVZEuWLGHTpk3pXUay7Nmzh5UrV3Lx4kUuXrzIX3/9ld4lJdnBgwf58ssv+fTTT9O7lCRp06aN5fdw8eJFjh07Rp48efh/e/cen3P9/3H8cV07YbY5z0xMcz6GkCIdVORYiYpOTulLdFhO1RcdUCFE4kslUUgRlUKRyqHGz0SOmcPMMeyAzXZdvz9ml82ubde1Xcfteb/dru+363N9Dq/r2vZ5en8+n+vzatOmjbtLExdTbnkWZZdnUW55Dk/ILV+XbakIe/LJJ+nRowdbt251dykFtnfvXsaOHctDDz0EQP/+/enevbt7iyqApKQk3n77bRo0aABAo0aNOH/+vHuLKqB///2XUaNGUbt2bXeXUmBpaWns3r2bNm3aULp0aXeXUyhms5mhQ4cyePBgbrzxRneXUyD+/v74+/tbnv/vf/+ja9eu1KhRw41ViTsotzyLsstzKLc8iyfkls4sOcCMGTO89mhWpo4dO9K/f3/L8/379xMZGenGigqmatWqPPLIIwBcuXKFadOm0bVrVzdXVTCjRo2ia9eutGjRwt2lFNjOnTsxm820bt2a8uXL061bN44ePerusgrk448/JiYmhurVq/P9999z5coVd5dUKJcvX2bmzJlERUW5uxRxA+WWZ1F2eQ7lludyV25psOQARe2obGpqKtOmTWPAgAHuLqXAYmJiqFGjBuvWrfPKU88bNmxg/fr1vPHGG+4upVD27t1LvXr1+OSTT4iOjsbPz4/nnnvO3WXZLSkpiXHjxhEZGcnx48eZPn069957L5cvX3Z3aQW2ePFiWrZsSfXq1d1diriBcsszKbvcT7nludyVWxosSQ7jxo0jMDCQvn37uruUAmvUqBGrVq2iXr16DBo0yN3l2OXy5csMHTqUadOmERwc7O5yCuWRRx5h/fr1NG/enIiICKZMmcK6detISEhwd2l2WbFiBRcvXuS7775j5MiRrFy5kgsXLrBo0SJ3l1Zg8+bNo1+/fu4uQ8QhikJugbLLEyi3PJe7ckvfWZJs1q1bx7x58/j555/x8/NzdzkFZjAYuOmmm5gzZw5169bl3LlzlC1b1t1l2WTixIk0a9aMDh06uLsUhwsJCcFkMnHixAmvCtPjx49z8803U65cOQB8fX1p2LAhsbGx7i2sgA4ePMg///zDXXfd5e5SRAqtqOQWKLs8kXLLM7gzt3RmSSwOHTpE3759mTp1KvXq1XN3OQWyfv16Ro8ebXnu4+MDgNHoPb/qS5Ys4dtvv6VKlSpUqVKFJUuW8MILL/D888+7uzS7jRgxgmXLllmeR0dHYzQaqVq1qhursl94eHiOSxeOHDlCtWrV3FRR4Xz11Vd06NDB6/9hKVIUcguUXZ5EueWZ3JlbOrMkAFy6dIkePXrQpUsXOnfuTFJSEgCBgYEYDAY3V2e7OnXq8Mgjj1CzZk3uvfdexo0bx913301ISIi7S7PZmjVrSEtLszwfPXo0LVq0oE+fPm6sqmAaN27MuHHjCA0NJS0tjaioKPr06UOpUqXcXZpdOnToQFRUFHPnzqVjx46sWLGCmJgY7rnnHneXViBr1qzxyt8nkayKSm6BssuTKLc8kztzy3sOWYhTrV27lj179vDxxx8TGhpqeRw5csTdpdklLCyMBQsWMHPmTG6++WYuXbrE3Llz3V2WXcLDw6levbrlERgYSPny5alQoYK7S7Nb79696d69O7169eKZZ57hrrvuYtKkSe4uy27lypVj+fLlfP755zRp0oSZM2cyf/58r7w5wqVLl/jjjz9o1aqVu0sRKZSiklug7PIkyi3P4+7cMiQnJ5vdsmUREREREREPpjNLIiIiIiIiVmiwJCIiIiIiYoUGSyIiIiIiIlZosCQiIiIiImKFBksiIiIiIiJWaLAkIiIiIiJihQZLIiIiIiIiVmiwJOIGv/zyC/Xq1XP5Om3d7oIFC+jQoYOjShMRES+n3JLiSoMlkUKoV68ev/zyi7vLAODWW29ly5Ytdi/nSe9BREScy5P2+cot8QYaLIkUEb6+vgQHB7u7DBEREZsot8QbaLAkxdqCBQu4/fbb6dmzJ2FhYXTr1o34+HjL69HR0bRr146wsDAeffRRLly4AEC3bt0IDAzkyJEjdOzYkcDAQCZNmmRZbsWKFTRp0oSKFSvSsWNHjh8/XuAaTSYTlSpV4tChQ8yYMYPq1asD0KdPH6ZPn26ZL7dLFT755BNq1apFZGQka9assUzP7z0AfPzxx0RERFC9enW+/vrrAr8HERFxDOWWcktcS4MlKfaio6Np2bIlmzZtIiAggGHDhgFw/vx5HnjgATp06MDWrVu5ePEio0aNAmDRokXExcVRtWpVvvzyS+Li4njuuecA+Pfff3nqqacYPnw4MTExlC1blrfffrvA9RmNRho2bMj+/fvZt28ftWrV4uzZsxw4cIAmTZrkuWxMTAwvvvgi7733HitWrOCrr76yvJbXewD4+++/Wb58OWvXrqVPnz6MHDmywO9BREQcR7ml3BLX8XV3ASLuFh4ezksvvYTBYGD06NHcfvvtpKWl8f333+Pr68vIkSMxGAwMGTKE/v37AxAYGAhkBEJgYCBlypSxrC8oKIg9e/YQFBTEtm3bSE1NZf/+/YWqsUmTJhw4cICjR49yxx13sG/fPg4dOpRv6KxatYq77rqLzp07A/D8888zZcqUfN8DQFJSEnPmzCE0NJSnnnqKqVOnFuo9iIiIYyi3lFviOhosSbEXHh6OwWAAoEqVKqSnp3P27FmOHz/OmTNnCA8PBzIuK0hMTOTy5cuUKFEi1/WZzWb++9//snLlSurWrUtwcDAmk6lQNTZp0oSYmBjS0tKoVasWv/zyCxUrVswRFNc7ceIEVatWtTyPiIiweZt16tQhNDQUAH9//4KULSIiTqDcsk65Jc6gy/Ck2Dt69KglFI4dO4avry8VKlQgPDycZs2asWnTJjZt2sTmzZv5/fff8fPzsyxrNBoxm83Z1rd48WJ+/fVX9u3bx08//cT9999f6BqbNGnC1q1bCQoKombNmqxcuTLfo3MAFStWzHYt+7Fjx3LMY+09APrSrYiIh1JuKbfEdTRYkmIvPj6eSZMmERsby4QJE+jcuTM+Pj506NCBI0eO8Oeff+Lj48PSpUvp3r17th30jTfeyJo1a4iPj+fnn38GIDk5GYBz587xww8/MHHiRKs7dXvUr1+fv/76i8jISCIjI9m+fTs33XRTvst17tyZtWvXsnr1anbv3m31kgRr70FERDyXcku5Ja6jwZIUey1btmTHjh20bt2a1NRUJk+eDECZMmVYunQp06dPp2nTpqxcuZKlS5fi63vt6tXx48ezZs0aGjRowIQJEwB47LHHiIyMpFmzZkyYMIF+/fqxd+9eLl++XOAaAwICqFOnDjVr1qRMmTJUqFDBpiN0TZs2ZeLEiQwZMoQHH3yQe+65J8c81t6DiIh4LuWWcktcx5CcnFy4QwciXmzBggUsXLiQ1atXu7sUERGRfCm3RFxLZ5aKkcOHDxMYGMjhw4cdvu4OHTrw1ltvFXh5Z9ZWHNWrV48FCxbYvVxhf44Aa9euJTAwMMejMEcobREYGOjSju4F/YxFJHfKqeLDnTmVad26dTRs2NDqa7Nnz7bc7KJWrVosXrzYIdvMy8CBAxk4cKDTt5PprbfeokOHDi7bnrfSYEkcYvr06fTt27fAy4eFhbFx40bCwsIcWFX+Hn/8ccvRuV9++aXI/ON36dKluX5B9/Dhww4LGmt27NhB8+bN2bhxY7ZHQECA07bpLDt27GDGjBnuLkNEHMBbcyqrzANCReGskjtzCmDv3r307duX9PT0HK8tXLiQqKgo7r33XmbOnMmNN95I//79iYmJcWpNzvLWW29pkF8IGiyJQ9SuXbtQAeLv70+zZs3ceqvPjRs3snDhQrdt35EaNmxI+fLlrb52+PBhxo8f77Rtx8TE0KJFC5o1a5btkXmbW28SExPDzJkz3V2GiDiAcsqzuDOn/vzzT9q3b0/16tVzvJaens6bb77JG2+8wfTp03n88cdZtmwZAQEBfPPNN06ryZnGjx+vwVIhaLAkIg71f//3fzRr1szdZYiIiFj166+/Mn78eAYMGJDjNYPBwOeff86zzz5rmVa6dGkCAgJITU11ZZniITRYKsJOnTpFr169KF++PA0bNuSnn37KMc/s2bNp2LAhFStWpH379vzf//1fttd/++032rVrR7ly5WjUqBEffPCB1W3ldg1xSkoKUVFRREZGUrFiRe68807+/PPPHPPldS34yZMnefLJJwkNDaV69eoMHz6clJQUy+uBgYGsW7eOESNGULVqVapXr867776b38eTrfbAwEDGjx/Pxo0bLd+xyXpJXtb6FixYQMuWLbPtSAHWr1/P7bffTrly5WjWrBlLlizJ9npsbCy9evWiUqVK1KpVi7Fjx5KWlmZTjZMnT6ZWrVoAzJ8/n8DAQP7880/OnTtHYGAgP/zwQ7b5rV0L/tZbbxEYGEjHjh0BLO/T2vXR8+fPp169elSuXJmnnnrK5u8bXbhwgYMHDzJ79mzCwsIIDw+nb9++nDhxItt8c+fOpUmTJpQvX54mTZrYfaQ0OTmZZ555htDQUGrWrMnnn3+eY55ly5Zx8803U758eVq3bp3t9rIDBw6ka9euPPPMM1SsWJFGjRrx8ccfZ3s9MDCQQYMGceTIEctnZe13/N1336VGjRqEh4fz0ksvFfp2uyLFiXLKNsopx+UUwNChQ3n88cetvmY0GrnpppuyXTq+ZcsWzp8/T+vWrS3Tdu7cyf3330/lypWpXr06gwYNstyC3VafffYZdevWpUKFCvTv3z/b7wzk/fP45ZdfCAwM5MMPP6R27dpUrVqV/v37c+HChWyvBwYGAtCxY0cCAwOpV69ejjq2bNnCbbfdRoUKFbjrrrs4dOiQXe+jqPPNfxbxVgMGDODvv/9m3rx5XLp0iREjRmR7ff78+YwYMYIJEybQsGFDPvjgAzp16kRMTAzly5dnx44ddOnShc6dOzNu3Dh27drFyJEjCQoKynUnc71Jkybx6aef8v7771OxYkXmzZtHz549OXjwoE2XZV2+fJlOnTphNpv56KOPOHfuHK+88gpxcXHZ/oH9yiuvUKVKFT755BN++OEHxo4dS8eOHXP94mZW06dPJykpiY8++ojt27fz/vvvA1g9PT99+nRWr17NU089le3syb59++jevTtPPfUU48ePZ8OGDTz99NOW4E1NTaVbt26EhYXxxRdfcOLECV544QWMRiP//e9/862xWbNmHD9+nISEBHbu3EmzZs3466+/uHLliuX1/PTt25eOHTuyfft2hg4dysaNGwFyXAaxcuVKLl26xNtvv82///7LsGHDuOWWWxg0aFC+29i2bRtms5mmTZsyZswY4uPjGTt2LI8//jhr1qwBMnbgw4YN45VXXuH2229n/fr1DBo0iGbNmlndiVszatQoVq5cyeTJkwkJCcnxu71+/XqefPJJRo0aRbt27Vi6dCkPPPAAW7ZsoU6dOgD89NNP3HfffSxevJgNGzYwZMgQQkNDuf/++3nllVcYNGgQ33//PR999BFLly4FyHEJz8yZMylRogSzZs1i586djB07lvbt21uCXkTyppxSTmXlipyCjAGRPd544w3q1q3LfffdZ5nWq1cvqlatyuLFizlz5gwjRoxg4sSJvPHGGzatc8uWLTzzzDOWg3cfffQR33zzDb169QKw+efxzjvvWAbeL7/8Ms8++yyLFi2iadOmls+vbdu2ltvJX38Z6dGjR3nssccYMWIE1apVY+jQobz66qtF5nJPR9BgqYjau3cva9euZcGCBXTv3h3IaDb38ssvW+aZOHEiAwYMsBx5atSoEdWqVePbb7/liSeeYOrUqdxwww189NFH+Pr6cscdd3D69GnOnTtncx2HDx/mhhtuoGfPnhgMBho1asSff/5JWlpato7iuVm6dCl79+4lOjqa2rVrA1CqVCkef/xxYmJiaNy4MZBx2nzp0qX4+Phw5513smDBAv766y+bQihzvd9//z0HDhzIc4f+ww8/sH79eipWrJht+qRJk6hXr56leV6bNm347rvvWLRoEXfeeSeLFy/m8OHDrF271rJsTEwMixYtsjmEDAYDe/bsYdeuXXTr1o2//voLs9lMtWrVctRjTVhYGGFhYSQlJVnWac2BAwfYsWMH4eHhAHzzzTfs3Lkz3/VnrvP333/P1ksjPDyczp07W34emUdl+/XrR2hoKLfeeivNmzenbNmyNm0jKSmJ+fPnM3bsWPr06QNkBF/Pnj0t80ycOJFOnTrxyiuvAHDrrbeyfPlyli5dyquvvgpkdHr/5JNPCAoK4q677mLz5s188MEH3H///VSvXp3q1auza9cuy/cUrDl9+jQ7duygdOnSdOjQgcWLF7Nz504NlkRsoJxSTl3PFTllr/nz5/Pzzz/z9ddfWwZZaWlpxMXF0a9fP9q1awdAZGSk1ZtF5OaDDz6gXr16vPfee0BGTmU9YGjrz+PNN9/koYceAjKuunj22WeJi4sjPDw82+dXq1Ytq59nbGwsixYtolu3bgA8++yz2a60EA2WiqwDBw4A0KJFC8u02267zfLfiYmJHDlyhA8++CDHJQsHDx4EMv4oW7Vqla2Z3dixY+2q44knnuDrr7+mZcuWtG3blltuuYWuXbvaFECQcaYiPDzcEhQAd9xxh+W1zBAaMGAAPj4+APj4+FC2bFnL0SxHGjt2rNUd/q5du4iJibGc7s6UeRp/165dXLlyhYiIiBzLpqam5vuF4ZCQEG688Ub+/vtv9u3bx9tvv83w4cMxGo00b9684G/Iis6dO1sCCKBChQo2f5YhISE5mg5mXrYQExNDw4YNuf/++6lWrRq33XYbd955Jy1atKBbt26EhobatI1Dhw6RlpaW6+82ZHze//77b46fR+bfBWR8uTgoKMjyvFmzZqxYscKmGjL16dOH0qVLW57b81mJFHfKKeVUQRUmp+yxf/9+RowYwYABA7j33nst0319fenfvz9vvPEGv/76K82bN+e+++7L9rtsy7pvvvlmy/OAgIBsy+f388iU9dLAzM/5n3/+yfb55KVy5cqWgRIox6zRYKmIMplMAJYd8/X/nWnMmDHZTisDVKpUCQCz2ZzjEoQjR45w6tSpbH/geWnTpg0xMTH89NNPbN68mZdeeonJkyezYcMGSpQoke/y1mrIPLKT9bshNWrUsKmewsprR9ilSxdGjRqVbVqpUqUs/x0eHm65nCurrCGfl6ZNm/Lzzz8TEhJCnTp1+Pvvv/H397cc1XKUG2+8scDLxsbGkpycTIMGDSzTMo/wZl6LXb58eaKjo1m3bh2bNm3igw8+YMyYMaxfv95yiVxebP3dHjhwIE899VS2aWXKlLH89/XfLTKZTHZfmlGYz0qkuFNOOYdyyjGSk5N57LHHqFGjBhMnTszx+uTJk3nsscf45Zdf2LhxIxMnTmTMmDHZzozmxWQy5fh9v/65LT+PrL9jmX9T9mSZcix/usFDEZX5y79t2zbLtM2bN1v+OygoiBtuuIGzZ8/SpEkTy2P+/Pn8/vvvADRu3JgtW7ZkO638xhtvMGzYMJvrmDJlCgcPHqR37968//77fPnll/z1119s2bLFpuWbNWvGsWPHsp0RWL9+veW1TNYC1l4BAQFcunSpQMvWr1+fo0ePZvssN2/ebNnJ1a9fn9OnTxMeHm55/dy5c7z//vs2f3k288xHrVq1CAgIICgoiI0bN9p9xC4z/HN7r4X5LGfPns3zzz+fbVrmdc+ZAb5ixQqWLl1Kly5dGD9+vOX30logWFOjRg2MRmO23+1NmzZlm6d+/fqcOHEi289j1apV2XqT7Ny5k4SEBMvz6OhoIiMjs62nRIkSef5O2Du4EpFrlFP2U05lcMRnmZf09HQef/xx4uPjWbRoUY5Bc1xcHC+//DINGjTghRde4KuvvuLpp59m3rx5Nm8jMjIy2+9+WlpathuL2Prz+O233yz/HR0djcFgyDEAyuv3xtmfZVGgM0tFVIMGDWjbti3Dhw/HbDZz8eLFHF86HDVqFC+88AKVK1emZcuWfPPNN8ybN49HH30UgOeff5677rqLvn370rdvX3bv3s2XX37J5MmTba7j0KFDfP7554wZM4Zy5coxf/58fHx8qFatmk3L9+zZk+nTp/PII4/w+uuvc+7cOUaPHk23bt1yXO5VWC1atODNN99kxYoVBAUF8ffffzN48GCblo2KiqJVq1YMGTKEnj17sn//fkaNGmW5HKRXr15MmjSJRx99lKioKK5cucKIESOoX7++zT07mjVrRmpqquVuQ7Vq1SI2NpamTZva9T7r1q1LUFAQ7733Hrfffju7du2ie/fuNl8Gl5cnn3ySOXPm0L9/f+666y527NjBrFmzePDBBy3X5aekpDBy5EgA6tSpw9atW0lMTLT5qGtwcDCPPPIIb7/9NuXKlSM4ODjHl8JHjhxJly5dLDdc2LRpExMmTOCzzz6zzHPp0iWeeuophgwZws8//8ymTZv46quvsq2nadOm/Pvvv3z00UfUrFmTP/74w/IFWxEpHOWU/ZRThc8pW0yePJkffviBMWPGcO7cOcsVEqVLl6Z27dqEhITwxRdfkJKSQs+ePUlMTGTDhg12nT185pln6NixI8OHD+f+++/n008/5fjx45bXbf15jB07llKlSmE2mxk3bhwPPvhgjpsRtWjRwvId3WPHjnHDDTdku3xP8qbEL8I+/fRTmjZtysCBAxkzZgzDhw/P9vqTTz7J+PHjmTdvHl27dmXjxo0sXrzYcgbgpptuYuXKlcTGxvLAAw8we/Zs3n77bbs6oI8fP55bb72VF154gS5duhAdHc0nn3xi8w6lRIkSfPvtt9SvX5+nnnqKUaNG0bNnTz766CPbPwgbtWvXjtGjRzNs2DAefPDBHLc5zUudOnVYvnw5MTExdOvWjcmTJ/Pqq68ydOhQAEszu7Jly/L4448zePBg7rzzTubOnWvzNpo2bYrRaMwWQpGRkYSEhNj1PoODg/n4449ZvHgxnTp14v3337ecui+sunXrWm5yMGTIEL7//nveeustPvnkE8s8PXv2ZOTIkUyZMoVOnToxY8YMRo4cyWOPPWbzdt577z26dOnCiBEjGDJkCC+88EK21++8807mz5/PqlWr6Nq1K59//jmzZs2yfIk8c55q1arxyCOPsGjRIt59990cl/rUrFmT999/n7fffpsuXbpYvUW5iBSccso+yinXWLZsGQDjxo2jbdu2lkfmZ1W6dGm++uor9u/fT8+ePenXrx+1a9fmww8/tHkbt99+O3PnzuXHH3+kR48epKSkZMsoW38eY8aM4dVXX2XQoEG0adPGcgOPrGbMmMGZM2fo3LkzL7/8crarKiR/huTkZDUFEZFiZeDAgZw9e9YSiCIiIt7kl19+oWPHjhw+fJgKFSq4u5wiTWeWRERERERErNCZJRERERERESvcdmbp7NmzbN68mTNnzrirBBEREbsou0REihe3DJaWLl1K48aNeeGFF6hbt65NtwzeuHEjzZo1o1q1akyfPt0FVYqIiFyj7BIRKX5cPlg6f/48UVFR/Pjjj2zatInp06fz2muv5bnM6dOn6dmzJw8//DA//fQTixcvZsOGDS6qWEREijtll4hI8eTywVJSUhJvv/02DRo0AKBRo0acP38+z2UWL15M5cqVGTlyJDVr1mTUqFHMnz/fBdWKiIgou0REiiu33uDhypUrPPvssxiNRubMmZPrfM888wwlS5a03Ds+Pj6ezp07Ex0dbXX+lJQUUlJSLM9NJhP//vsv5cuXx2AwOPQ9iIgUd2azmaSkJMLCwopFw15ll4iId7Mnt3xdVFMOMTEx3H///fj5+bF9+/Y8501MTKRu3bqW58HBwdm6HF9v0qRJjB8/3mG1iohI/vbt20d4eLi7y3AqZZeISNFhS2657cyS2Wxmx44djB49muDgYL744otc533iiSe45ZZb+M9//gNAeno65cqV48KFC1bnv/7oXEJCAnXq1OHoXxAc5Nj3IW52BPgV2Hj1/0XE5RLMcEMiHD9+nJCQEHeX41TKLnGIrLl1xM21iBRD9uSW284sGQwGbrrpJubMmUPdunU5d+4cZcuWtTpv2bJls92mNTExEX9//1zXHRAQQEBAQI7pwUEQHFz42sWDlAZKAH6ArlIRcavicKmYskscohQQAPig7BJxI1tyy+UXl69fv57Ro0dbnvv4+GQUksf1gs2aNWPr1q2W5zExMVSpUsV5RYqIiGSh7BIRKZ5cPliqU6cOH330ER999BHHjh3jv//9L3fffTchISEkJCRw5cqVHMt06tSJTZs2sWHDBtLS0pg2bRrt27d3dekiIlJMKbtERIonlw+WwsLCWLBgATNnzuTmm2/m0qVLzJ07F4BWrVqxevXqHMtUqFCBCRMm0K1bNyIjI9m9ezfDhw93dekiIlJMKbtERIont9463F4HDx5k7969tGnThmA7LuBOSEggLCyMC4fzvu473VSKK2kV0AXEXiQO2Jrl4W3MZvzOnsHn0kV3VyJSYAlmCEnIuDW2Pfvm4sKZ2WUy+5J6JQw3HPuUwsiaW3FursVeyi0pAuzJLbfd4KEgIiMjiYyMdPh6zWYDJ84/zfnkrmDwR4MlL2IEbgLqAj3dW0rBmCE1lTKrv6Hyoo8xmL3m2IWI2MhZ2ZWaVolDJydhQgf5vE4YcB9wB5DzCk4Pp9yS4sWrBkvOcuL805y/+CiVKpWhVEkoBjd0KjquAMlZHl7GDFwETj30KABhCz9yaz0i4h3MZgPx/w7Cx68GN4SVwKjc8i5Zc8vLBkvKLSluiv1gKd0UyPnkrlSqVIby5dxdjdjNCKTi1bdfLQlQpgynOnSl0ldf6NIGEclXWnoZLl65mSphJShV0t3ViN2uAClkZJgXZpdyS4qTYn+R85W08mDwV9iIW5UC8PfnSvkK7i5FRLxAuikI8CWPtk0iTqXckuKi2A+WMg7pGHTpnbiVIfN/9YsoIja5ml3uLkOKLeWWFBcaLImIiIiIiFihwVIRFht7HINfC3eXISIiYhPlloh4Gg2WxGOt3xBNRM2u7i5DRETEJsotkaJHg6VCSk+HimFNGDk6nMREfZwiIuLZlFsiIrbTXrKQzGbw9zcz+b1Q6jdu6NLw2b59L63b9KV0mdu57fZ+7Np10KblNv66nZuaP0ap4Da0uOUJ/vrrgOW1H9dspl6jhykV3Ibbbu/HwYPHLK99tvA7Imp2JTCkLR07D+Xs2fM2bW/SlAWE3dCB4HJ38GifV0hJSQXAbDbz7uQFVI/sQtgNHZg2/XMATpw4g8GvBXe2H8Thw/EY/Fpg8GvBiRNnAEhPT+e/Yz8k7IYORNTryqxPvrRsKyU1lSdeG0OZ2++k0t338vYn8y2vJSYn03PEKILb3kGVezsy44slNtUvIlKUKLfy59TcqtmVWR8pt0S8hQZLDpKWZuTYMX+XhU9CQhIdOg+la5e27N31Jbe0akjvJ17LdzmTyUSPXiN5uEd7/tm3nFtbN+blkdMtrz/x9Bj6Pd2VfbuX0bBBJK+OmQVAUtJFnu7/OhPfGsLumCX4+vowacpn+W5vz55YRo6eyeJF49m2dQEHDhzlk/mrAPhs4fdMePsTvvjsLb5a8g6vjvmQX3/9P0JDy3Pu9E+sXD6FG24I5dzpnzh3+idCQ8sDMHX653yxZA3frpjKJ7PHMObdOSxfsx6Aj79ZyaaYnfzx2XxWTXuPN/43jz2HYgEYN+d/xB4/zl9LvuCLCW/xwpQpHD992p6PXUSkyFBuWef03Jo3hjET57D8h/WAckvE0xX7prSOljV8Fn5ent6PnuWVUfEEBZkcup1V3/5KuXLBjBrxNACvju5HyxYNbFp2R/RCQkJKExNzgMTEi+zbf8TyWsmSJUhJuUJISGk+/GAUJlNG3T4+Rvz8fElJSaVSpbJ88/UUzGZzvtsqUSKjCUhKyhWqVavMlt8/sbw2f8EqBvbvTuvWjQHo3KkN36z6hTZtbqJMmSBKly6F0WikTJmgbOucM/drxr42gGbN6kIqPD/wUT78fBndb7mDkgEBmMwmrqSl0aJBfS788jNGY0b4v9KvL+MG+XHy7FkOHjuGj9GHg0ePUaViRZs+NxGRoki5lZ3Tcwt4ftCjfLhwGd1vVW6JeDqdWXKSzPCZMjWU5i3rY8P+2S7H4k4RUT3M8rxs2WB69bw33+WMRiNTpi4ivHonBg99hwsJSaSnXwvEzxe8yfoN0YTd0JE27fqzbdseICOMln4xkTlzv6Zi2L106DSUf/6Jy3d7ERFVmDv7FUaOnkH50Pb0emw0Z86cByDu+Gmmz1hMmQp3UqbCnXy9fD1Hjp7Id51Hj57kxhrhluc3Vg/naPxJAB7r0IGH299N52EvUPmeDoycPsMSjtv37OWmR3pz58BnWbt1K6VKlCDd5Nh/DIiIeCvlVgaX5dZx5ZaIN9BgyUl8fU1UrZrKSy+cJHrrbof3bLuhaiiHYo9bniclXaThTb0s10fnZv2GaGbNXsbfO5fw55ZP6fd0N8trycmXSL54iTWrZ/LvqXW0bXMTfQe8AcDZs+cpWyaI336Zx8m4H6hUsSwvRE3Jt85jx07SuFEtorcu4Mg/Kzl3LoE33poHQNXwSrw+9hn+78+F/N+fC9kRvZAJbw62LGs0GqweBaxWrTL/HLoWeAdjj1GtSmUA9sTG8sxDD/LPyhVsnDeHz77/nq9/Xg9A33Fv8Pxjj3D4u5UseON1/P10YlVEJJNyK4NyS0Sy0mDJwTLDJurFk+yO+YsJb8U5/FIGgE7338a5c4mMn/gxx46d5M3x80hPN1muj85NUtJFAC5cSOK333bw4svvWXbsJpOJTl1f4LOF33HmzHmMRiMmc0btZ86c5+77BrP6h99JSEjOeM2U/2HH3X8fomOXYfz22w6Sky9hMBgsl0g8+XgnPv/iRxITL3Lx4mUGPjuembOWWpa9sUY4x4+fJjr6bw4cOMr27XsBGNjvAca+8T+2b9/Lho3RTPvfFwx69CEAPl/9A0+PfZ3d//xDWno6ZrPZsr2kSxe5nJpK7PHjDHprAifP/mvTJRkiIkWZcis7p+fWL9FMm/0Fg/oot0S8gQZLDuKqsMkUHFya1aums3LVRuo16snmLX/x9dJ3MeRzKLDDfa3p2rktzVo+zqDBExjQrzvHj5/m5MmzBAUF8tn813lr4sdE1nmAlas2MmvGSADq1Ilg8jvDeHbI29xYuzt79x3mnQnP5VvnvffcwjMDHuDhR0dSu/5DmM1mXhnVF4Dej3WkV8976NTteW69vR81Iqrw+thBlmWrVg3lnQlDua/TczRr+Ti/b9oBwLChj/BIz3vo2GUoTwwYy9ioAXRr3w6AEU89SWi5ctz2dH9ufaof3dq148G77gRg+stRTFrwGbc8+TTly4TQsGYk2/futf/DFxEpApRb1jk9t54ey9gRA+h2r3JLxBsYkpOTi/whioSEBMLCwrhwGIKDs792ObU6h05/SI3qFShRwv51p6dDWNUm9Ot7htEjHf+FWMlHKpCY5eGlLpvh0Okz1IgaRImjh91djohdEswQkgDx8fEEX7+TlQJzVnYptzxA1txKdXMtBaTcEm9mT27p4tdC8vGBU/E73F2GiIiITZRbIiK202V4IiIiIiIiVmiwJCIiIiIiYoUGSyIiIiIiIlZosCQiIiIiImKFBksiIiIiIiJWaLAkIiIiIiJihQZLIiIiIiIiVmiwVITFxh7H4NfC3WW4xVNjxjL2wzkev04REblGuaXcEvE0akorRdIHI0diNBrcXYaIiIhNlFsinklnlgop3ZROxSn3MPKn90lMSXZ3OXJVqZIlKBEQ4O4yREQ8jnLLMym3RDyTBkuFZMaMv48fk7cspP7sni4Nn+3b99K6TV9Kl7md227vx65dB21abuOv27mp+WOUCm5Di1ue4K+/Dlhe+3HNZuo1ephSwW247fZ+HDx4zPLaZwu/I6JmVwJD2tKx81DOnj2f77YGPPMmr42ZZXm+ctUvNG76qOX5pwu+pVa9B6lQuT2jX52J2Wy2vGbwa8GuXQd55tnxlKt0NxcuJAFgNpt56eX3qFC5PWXD72LYq5OyLQfWLz1IS0tj9PszqXzPfYTd24Exs2ZbXktPT+e/sz4k7N4ORHTqyqylX+b73vJbLvb4cQzNWnD63Dl6jhhFtfs727ROERFnUm7lzem5VfEuho1Ubol4Cw2WHCTNlM6xxFMuC5+EhCQ6dB5K1y5t2bvrS25p1ZDeT7yW73Imk4kevUbycI/2/LNvObe2bszLI6dbXn/i6TH0e7or+3Yvo2GDSF69GhhJSRd5uv/rTHxrCLtjluDr68OkKZ/lu70eD93N96s3WZ6v/mETDz14F5ARfgMGvcXUyS+yfu2HLFj4HQsXfZ9t+f7PvEVwcCBff/kOgYElAPjhx018PH8V6374gF/X/I+vv1/Pj79uzreWSQs+4+uf1/PDzPf5dtpUPlz2Fct/Xg/A1EWf88UPa/h22lQ+GTeGMR/OsbyWF1uWe/Cl4TSvV5fPx7+V7/pERFxFuWWd03Nr/f/4+tv1/PiLckvEG+g7Sw6WNXwW/rWa3g078MptfQkKCHTodlZ9+yvlygUzasTTALw6uh8tWzSwadkd0QsJCSlNTMwBEhMvsm//EctrJUuWICXlCiEhpfnwg1GYTCYAfHyM+Pn5kpKSSqVKZfnm6yk5jopZc9edLYg9/BonT54lNLQ8P6zZzPIv3wVg/qff8kD3O+h0fxsA+jzWkW9WbaRP7/styzduVJN33x6WbZ0lSwZgMplISblCw0aRxP7xDYYkAyTlXctHK1byxrPP0KR2bQC+fGci5UKCAZjz1deMfWYAzerVBeD5xx7lwy+X0f3OO/Jcpy3L3d/mVkY89WTexYmIuIlyKzun51bDSGJ3KLdEvIXOLDlJZvhM2bKI5vMet2kHbY9jcaeIqB5meV62bDC9et6b73JGo5EpUxcRXr0Tg4e+w4WEJNLTTZbXP1/wJus3RBN2Q0fatOvPtm17gIwwWvrFRObM/ZqKYffSodNQ/vknLt/t+fn50vn+Nvzw42b27z+Cj4+Rhg1rAhB3/BTLV2ygTIU7KVPhTqa9/wVHjpzItvzQIb1yrLPd7c0ZPfJpnh4wjgrV7uGZl8dz8dLlfGs5duokEVWqWJ63bdaUBpGRABw9eZIbw8Mtr91YNZyjJ0/mu05blhv66CP5rkdExN2UWxmcnluV7+GZF5RbIt7CLYOlVatW0aBBA4KDg2nXrh179uzJd5kePXoQGBhoeXTq1MkFlRacr9GXqkGVeKlVb6L7LcBgcOwdbm6oGsqh2OOW50lJF2l4Uy9OnDiT53LrN0Qza/Yy/t65hD+3fEq/p7tZXktOvkTyxUusWT2Tf0+to22bm+g74A0Azp49T9kyQfz2yzxOxv1ApYpleSFqik21PvTgXXz/w+8ZlzI8cJdletXwSgwa+CD/9+dC/u/PheyIXsT8j8ZkWzYwsGSO9R04cJQundqya8cS/vrjCzZH/8WHny/Lt44bQkM5dPzaZ/bOJ5/y31kfAlCtcmX+ibsWogePHqNa5cr5rtOW5QJL5nwP4joms4F3E15m7aW7STH7F3rZwqxPvFtRzy7l1jVOza3/+4LNf/7Fh58pt8Q65ZZncflg6Z9//mHQoEG8/vrr7N+/n2rVqjF48OB8l9u+fTtbt24lLi6OuLg4lixZ4oJq7ZcZNlGterP7mSVMuGuIwy9lAOh0/22cO5fI+Ikfc+zYSd4cP4/0dBOhoeXzXC4p6SIAFy4k8dtvO3jx5fcsRw9NJhOdur7AZwu/48yZ8xiNRkzmjKN3Z86c5+77BrP6h99JSEjOeM1k21HHe++5hS1b/2LVd79mC50nHu/EipW/cOLEWdLS0nnlv7N45b+z8lhThp9+/oOHeo1g+/a9pKZewWAw2FTL0127MG72/9ixbx/Ru//m/cWLqVejBgADH3yAsbP/x/Y9e9kQHc20z79gUI+H8l1nQZcTVzLgQzqbUm/lg8TBdoaF9WULvj7xVkU5u5RbOSm3xL2UW57E5d9Z2rt3L2PHjuWhhzL+MPv370/37t3zXCYuLg6z2UyDBrZd2+wOvkZfKgeWo0/Djoy+7WmnBE1WwcGlWb1qOv957m0mvP0JzZvV5eul7+Z7JLDDfa3p2rktzVo+To2IKgzo152Ro2dYrs3+bP7rvDb2QwYMGk/NyKrMmjESgDp1Ipj8zjCeHfI2J06cpUnjWsyb86pNtQYE+HNLq0b8vimG5s3rWaa3bdOUsa8N4PGnx3DixFnuvqsFc2aNznd9Tz/VlW3b93Jfp+e4dCmFe9u14tnHHgJT3stFPd6HhKRk7v3Pc/gYjQzu+TCPdrgPgGGPPsL5xEQ6PjeUAD9/xj4zgG53tMu3loIuJ65nwocEcwibUm9l55XGNPKLoW2JjQQYUu1aNia1MSkEFGp94n2KYnYpt3Ln9Ny6oxXPPq7ckrwptzyDITk52bEXJdtp7ty5fPjhh/z555+5zrNixQqGDh2Kv78/58+f5/7772fq1KmULVvW6vwpKSmkpKRYnicmJlK7dm0uHIbg4OzzXk6tzqHTH1KjegVKlLC//nRTOmHTOtCvSTeXhI1cJxVIzPLwUpfNcOj0GWpEDaLE0cPuLqdIMZmNTE18nkRz9j9+I+mUNiTlGRa5LQtmIPs/8GxZX1GVYIaQBIiPjyf4+p1sEeXN2aXc8gBZc8tLdxXKLedRbjmfPbnl1hs8pKamMm3aNAYMGJDnfPv376dp06asWLGCjRs3cvjwYcaOHZvr/JMmTSIsLMzyqH31LjLO4GP04dQLa5x22YKIOMe1I2ytmZP0DPZ9lz3nkfDCrU+8ibdnl3JLxDspt9zDrYOlcePGERgYSN++ffOcLyoqiuXLl1O/fn3q1q3LG2+8wfLly/OcPz4+3vLYt2+fgysXEW9nJJ1gwwVa+29iYOnZ2Pdd9pyJUrj1iTdRdomIOyi33MNtfZbWrVvHvHnz+Pnnn/Hz87Nr2ZCQEM6cOUNKSgoBAQE5Xg8ICLA6XUSkMJcdGEknkCRSKEEqAYVen3gfZZeIuJpyy73cMlg6dOgQffv2ZerUqdSrVy/f+Xv37s2wYcNo2bIlANu2bSM0NFShIiI2K2zYZC57W8CvzEoaTJrZV2FTzCi7RMSVlFueweWDpUuXLtGjRw+6dOlC586dSUrKaF8dGBhIYmIiJUuWzHG0rmHDhowYMYJ33nmHM2fO8PrrrzNw4EBXly4iXslMOj609v+9AOGQc1mT2VCI9Ym3UnaJiOsotzyJywdLa9euZc+ePezZs4ePP/7YMn337t106NCBd955hy5dumRbJioqiiNHjtClSxcqVqzIgAEDiIqKcnXpIlIIJrOByYlRNPXb5rSddZrJyFuJrxFuPEbPwMUEG5MwGsy8HPxugdZnbdnCrE+8l7JLpPhRbgm4YbDUpUsXkpOTrb72999/W53u5+fHrFmzmDUr/8ZvIuKprjXKc15/BwMGzMSZqjI18UWqGOMs4SNSGMoukeJIuSVuvMGDiBRPrmmKZ8CMQeEjIiKFptwq3tx663BxrtjY4xj8Wri7jHx9Mn8ld9z9jEPXOfbDOTw1ZqzLlhP7ZQ2fDxIHs/bS3aSY/R28FQNmjJbwmZvYjwRTaQdvQ0QcRbk11mXLif2UW8WTziyJ2z32aAd6PHS3Q9c58uknMZns765W0OWk4LI2xfs7rT5DSr/vhF4P147YTUt8gVeC3sCoQ0UiUkDKreJNuVW8aLBUSKZ0M5OrrqXp01VpO6oWAUH6SO3l7++Hv799/UryU6KAt+Yt6HJScNffGtU5TfHMGDBTxXiMnoFLFDhSrCm3Ck+5Vbwpt4oXffSFZTbj429g03uH+KDxBtaO/puUxDSXbHr79r20btOX0mVu57bb+7Fr10Gbltv463Zuav4YpYLb0OKWJ/jrrwOW135cs5l6jR6mVHAbbru9HwcPHrO89tnC74io2ZXAkLZ07DyUs2fP57utAc+8yWtjrn25eeWqX2jc9NFs8+R2OUNEza6sXbeF0a/OpHLV+9ix41o3+8+/+IHKVe+jVuMHeXncNCq1upcdWbrdW7ss4akxY3ntg1kMnvA2pW+7nfoP9eTvfw5lmye3yxmmLlxEtfs7U/7O9jw7fiJXrlz7GY+ZNZvQ9vdR5vY76Tv2ddLT0/P9XCRr5/Df+U/QTNqXXOeEOw2ZMWAi3HiU54Om0D/oI13/LaLcypPTc6veg7z832lUanYvO/Yot7yJcqt40mDJQUxpZhKOXXZZ+CQkJNGh81C6dmnL3l1fckurhvR+4rX86zSZ6NFrJA/3aM8/+5Zza+vGvDxyuuX1J54eQ7+nu7Jv9zIaNojk1auBkZR0kaf7v87Et4awO2YJvr4+TJryWb7b6/HQ3Xy/epPl+eofNvHQg3fZ/D5fGzOb48dP8/lnbxEZWRWA8+cTGfjseL5YOJ4xo/qz4MvviFm1iDrVq+e7vtnLvqZ0qVL8tfQLKpUry4SPP8l3mS9++JGJn8xn4Ztv8PvHc9kQvY2ZS5YAsOqXjby36HNWz5hO9MIFbNi2jS/XrrP5/RVHChsRz6Dcss7pufVqfxYs+Y6Y1YuoU0O55Q2UW8Wbzr07WNbw2fn5cRo9WsUplzms+vZXypULZtSIpwF4dXQ/WrZoYNOyO6IXEhJSmpiYAyQmXmTf/iOW10qWLEFKyhVCQkrz4QejMJlMAPj4GPHz8yUlJZVKlcryzddTMJvzv0b6rjtbEHv4NU6ePEtoaHl+WLOZ5V/afq//kJBAPvlobLZp+/YdISioFHe0a05Ks1Qe7z8GsxlKlMj/UoSqoZV4e9hzADzW4T4+/+HHfJf5aMU3vND7Mdo2awrAZ2++TtrVo3B3triZw99+Q3q6iU0xMfgYfdh35EheqyvGCtNkz/ZtmDEQbjxKz8AlChoRGyi3snN6bqWk8vhTV3MrIADy2RUqt9xJuSU6s+Q0lvCZeog5LTfatIO2x7G4U0RUD7M8L1s2mF497813OaPRyJSpiwiv3onBQ9/hQkIS6ekmy+ufL3iT9RuiCbuhI23a9Wfbtj1ARhgt/WIic+Z+TcWwe+nQaSj//BOX7/b8/HzpfH8bfvhxM/v3H8HHx0jDhjVtfp/PDe6VY1r16pU5dy6RgwePsXnrTsqVDSG0Qjmb1ndH8+aW//b387Pp53Ls5Ckiwq591s3q1aVlw4yAP3PuPA+89DJ1H+rBrC+XUapEQLbPU/JmMht4N+HlbHcUsnWaNb5GE2NCxmU7Imfrso6ez108vT7xXMqtDE7PrS1Xc6uicssbKbccz9Pr02DJSYy+BoKrlqD1CzUYuLUtBgd/+++GqqEcij1ueZ6UdJGGN/XixIkzeS63fkM0s2Yv4++dS/hzy6f0e7qb5bXk5EskX7zEmtUz+ffUOtq2uYm+A94A4OzZ85QtE8Rvv8zjZNwPVKpYlheipthU60MP3sX3P/yecSnDA7ZfygAQGFgyx7QSJQKoW6c6DZr04v4Hn2fWxBEYbfzmY3BgoF3bB7ghNJRDx6991gu/+57+r78JwJjZswktV47T69bw3fvTqG3DpYDF17XmfllvuZpzWoCN89m6Q7W+XefP5y6eXp94KuXWNU7NrS7PM2uScss7KLdcw7Pr02DJwSxh82IN/hPTjvZv1XPKnYY63X8b584lMn7ixxw7dpI3x88jPd1EaGj5PJdLSroIwIULSfz22w5efPk9y1Eqk8lEp64v8NnC7zhz5jxGoxGTOeNo05kz57n7vsGs/uF3EhKSM16z8Val995zC1u2/sWq7361O3SsWfbVT4SHV2L7H59xdO8qena7p9DrzMvT3bowddHn/Lr9/9hzKJZ3P11AZNVwAJIuXiLdZOLEmbPM+GIJX/30s8OPxhY1WftUzEr8DynmgOzTkp7l8vXTrMxn7w7V1mUdPZ+7eHp94jmUWzk5NbcOraLnA8otb6Lccg1PrU+DJQdxVdhkCg4uzepV01m5aiP1GvVk85a/+Hrpu/keCexwX2u6dm5Ls5aPM2jwBAb0687x46c5efIsQUGBfDb/dd6a+DGRdR5g5aqNzJoxEoA6dSKY/M4wnh3yNjfW7s7efYd5Z8JzNtUaEODPLa0asXffYZo3r1fo935P+5b89vsObr29HxWq3UOF+u2Zu2R5odebm0fuu5cRTz7Jo6NfoW2/AdzRvDlRjz8OwGsD+rH38GHqPtSD9dHRPHLvPWzfu9dptRQlJnxIJIRU/LNPM4dw5fppVuZT+NjG0+sT91Fu5c6puVX5HirUbM/cz5cXer25UW45h3LLNTytPkNycnKRP5yQkJBAWFgYFw5DcHD21y6nVufQ6Q+pUb0CJUrYv25Lv4q+VWk7Uv0qXOHxJ/9L9ephPDe4J4YrBj79+Fs+X/Yj0QsWuLu0ArtshkOnz1AjahAljh52dzlOYTIbmZr4PInm4PxntpORNMoYL1htDJjfdjOX/U/gTKYlOW4+5zQpzJ+t79eR9SWYISQB4uPjCb5+JysF5qzsUm65XrbcMhj4dO63fL70R6K/WJDvDR48lXKrcJRb13h6bmkPWUhGHwMvxzv3dLpk1/uxDowYNYMpUxcC0LheLSaPet69RUkhmIHr9362TStMY8Acyzp6PjcETl48vT5xHeWW6+XIrfq1mPzq8+4tSgpBueUKnlKfBkvidTrcdysd7rs140kqkJjlIV7DSDqBJJFCCVIJsEwrbUjikrkEV7JMy22+zB2oPbdzzW1Zk9no0Pk8hafXJ1IcZMstUG55KeWWa3hafRosiYhLZd0J3hbwK7OSBpNm9s0y7TdmJf2H9GzTcs7nqLBx9nzu4un1iYh4C+WWa3hqfRosYQbM6EYw4k7mzP8t0r+IOZv7mcyGAk+zxmQ2MDkxiqZ+27LMZ3277ya8nO98tr4PV7D+3jynPnG1q9nl7jKk2FJuKbfyU1Ryq9jf4CHdFMi+4wupVCmc8rb1hxNPUkQuwztrhlNxcdQe+Bg+ly66uxyvlfkl0WRzYJ5Hp2ydz5N4cs26wYNz5JVdV9LKcuDkfKqEhRGij9z7ZM0t9/8JF4hyyzGUW+6hGzzYwceYTJnAbzh1+lGgDKVK4nFfcJM8XLn6SAdvPMRqBi4Cp86fp8zqbxQ4DpL1tqM7rzTOPXxsnM+TeGPN4ni+Pucp5fcnp8/cg59fCYzKLe+SCqQBJrwuu5RbzqHc8lzFfrAEULnMxwCcOtUVDP7kvJuJeKw04HKWh9cxQ2oqZVZ/Q+VFH7u7mCIntx20H2k2zefJO3JvrFkcx2AwE1ZuFodO1uTwkQoot7xMZmalkHHAz6sot5xJueV5NFjiauiU/YhKIV9wJU2h41XigP8Dtl59eBuzGb+zZ3Rkzsmu7aBb83daff4TONOm+dzVc8Ie3lizOIa/72lqVelLalplMPu4uxyxx9Ysjzg312Iv5ZZLKLc8hwZLWfgYL+Ljf8TdZYg9DMAl4Cxw1M21iMcqKj0nrPHGmsVxjIY0Svgdc3cZYq80IAE4gbJLrFJueQ4NlkSkyCoqPSes8caaRUQkb8otz6PBkogUOUWl54Q13liziIjkTbnluTRYEpEixLN7ThSON9YsIiJ5U255OmP+s4iIuFdmM761l+4mxeyf6zRblzUazLwc/C7tS67LcomDbetzBVtrFhERz6TcKjq5pTNLIuIFDPiQnu12o7cF/GZl2q82zWf9CFfObbjvSJgn1SIiIvZTbhWV3NJgSUS8RtYeDTFXGnPZHJB9WmpjUgjIdz5vafjnSbWIiIj9lFven1saLImI1zHhQ6I5hIxe8lmmYWWalfm8reGfJ9UiIiL2U255b27pO0si4sWsNWewbVrWpnhzkp7BbLaymB3zuYIn1SIiIgWh3PI2OrMkIl7MTM5AsW2aNzb886RaRESkIJRb3kaDJRHxOpk730vmElwhwDItkCRSKEFqlmnW5vO2hn+eVIuIiNhPueW9uaXBkoh4jaw739sCfmNW0n9IN/tmmfYrs5IGk5ZtWs75vKXhnyfVIiIi9lNueX9uabAkUpxFXH0URuzVh1PlbGxnMhsKPM3WbbiPJ9UiIuJh7nDAOmJxcnYpt4oKQ3Jyssu/arVq1SpGjBjB0aNHadq0KbNnz6Zu3bp5LrNx40aGDRvGmTNniIqKYujQoTZvLyEhgbCwMC4chuDgwlYvHiUWWJ/lIbaLwCGDJZPJwOTZUTQN2UbblGs7+smJUTT125Zt52/LtKKsMO/Xkz+rBDOEJEB8fDzBRXgnq+wSh1mf5RHrxjq8TQTKLRdTbrnhzNI///zDoEGDmDZtGm3atCEqKorBgwezbt26XJc5ffo0PXv2ZOjQoTz88MM8+eSTNGnShHbt2rmwcpEiJAKHhQ7/GPAxprPp9K3sNOTVeM+2ZnyetkN1rMI07Su6Df+8gbJLxMNEFGLZdAM+fulsOnMrO30b08ig3Mqdcsvlg6W9e/cyduxYHnroIQD69+9P9+7d81xm8eLFVK5cmZEjR2IwGBg1ahTz589X4Ih4kHwb79nYjM+bd6i2Ksz7LW6fladQdokUPSazDwlXQtiEcis/xTm3XD5Y6tixY7bn+/fvJzIyMs9ldu7cSbt27TBcvd9g8+bNGTNmTK7zp6SkkJKSYnmemJhYiIpFxB65Nt6zsRmfN+9Q7VWcw8fbKLtEii7llu2KY265tSltamoq06ZNY8CAAXnOl5iYSPXq1S3Pg4ODOX78eK7zT5o0ibCwMMujdu3aDqtZRGzl/MZ7RUVh3m9x+6w8gbJLpKhSbtmqOOWWW++GN27cOAIDA+nbt2+e8/n6+hIQEGB5XqJECS5evJjr/FFRUTz33HOW54mJiQodEZdzYOM9L2xiZ4/CvN/i9ll5AmWXSFGl3LJVccottw2W1q1bx7x58/j555/x8/PLc96yZcty5swZy/PExET8/f1znT8gICBbQImI6+TVUM/WZnzecmq+sArzfovbZ+UplF0iRY9yy3bFMbfcMlg6dOgQffv2ZerUqdSrVy/f+Zs1a8bSpUstz2NiYqhSpYozSxQRO+XfeM+2ZnzetAMtqOIYNkWBskukaDEa0intm2S5G55yK3fFObdcPli6dOkSPXr0oEuXLnTu3JmkpCQAAgMDSUxMpGTJkjmO1nXq1IkXX3yRDRs2cNtttzFt2jTat2/v6tJFxCoz6SYfWlf83dKvIs1kJNEcRLjxGD0DFxNsTCI13YdEcxClSeDxwE+o4HMu12Z87ya87JF9GaCwfSMK07Sv6Db88wbKLpEixHA1t2r+TtuAjQQcVW7lTrnl8qa0K1eu5JFHHskxfffu3XTo0IF33nmHLl265Hh9zpw5DB8+nJCQEEqVKsX69esJDQ21aZtq7FeExaKmtAURgeP6LMVe9wBMZiNTE58n2RxoOZrU2v93Jie9jBkDBsxUMcZZAikra8t60k7W0+tzh+LQlFbZJQ61Pssj1o11eJsInJZdyq3ixZ7ccvlgqTAOHjzI3r17adOmjV2BrMApwmLRYKkgInDJYCnRnPEHZySdQEMSieYgrt2E02w1fKwt60k7d0+vzx2Kw2CpMJRdksN6NFgqiAicPlhSbhUP9uSWW++GZ6/IyMh8+1qIiOex1q8CDJgxEGeqytTEFy3hU9pwMceyntyXwdPrE/dTdol4H+WWZHJrnyURKW6s96swYyTOVJVpiS9gMllf0tP7Mnh6fSIiUhDKreLOq84siYi3s96vIuOyhmP0DFyCMZd+C57el8HT6xMRkYJQbhV3GiyJiNNlv/Y7c2+cPWyyXvt9/bKefG21p9cnIiL2U25JJg2WRMRpsu6Qr91VyHrY5LWsJ+7MPb0+ERGxn3JLrqfBkog4Qc7eCmkmI2YMhBuP5hk2nt+XwdPrExER+ym3xDoNlkTEZilXjEwY/hqmqsd4tt9iwsokkZZm5K0VrxFe8hg9fTNupWo0mHk5+N1syxoNZkoZLhLhezjPHbW1Za1JMxl5K/G1bA0EbW28V5j5bK2vcE0ARUTEEXLNrWmvEV7mGD3DFhOMcsuebRQ3GiyJiM3MJgNmgxnjsap8+PqLmMPjGHD3lxgwE3epKlN5MdemfWDAh3QH3qo0o0lg1lu49ii11MZt2FpLYWp29PsVERF7Wc2tx6/m1vmqTD2v3HLe+y0aNFgSEbsZMGAwGzAfq8rcT4dlPM+l/0SOTucO7+9wbbvTk57HgMnmbTh6PmvUz0JExP2y5dZE5VZelFvZabAkIgVmCR+PaNpnuPoV3Gv3Ps1tG36k2VSLrfMpfEREvINyS7llLzWlFZH8xV59HLX+ssGjmvblrMXWbTh6PkcvKyIiNopFueWgmot7bunMkni3iCwPsU0E9n1u68kIHMjozWdlJ5lxW1VPadqXs4Fgjm3ksmSB57OjZjUBFJFs++BYN9bhTSKwPbtiszyUW4WuubjnlgZLUjREAHe4uQZvEoF9YRN7ddp1gWPGjNlgxmA2ZJvqnqZ9mRczmDBd3bXltg1ba3FkzepvISLZRKDsstcdNswTS87BUhbKLeWWvTRYEu8XQcYOMcKtVXiXiMItnhk25uBjDDAs4+Pzw64eF3NH075rIdej1Jd8lNyfZHOgTduwtRaFjYg4RYS7C/AiETbME0vOg3xXKbeUWwVl92Bp3759TJo0iWPHjmG+7qLF77//3mGFidglwt0FFA+Gq5ctmEKO8qxxCWGmJNLS3dW0z5xjuyazwcZt2FpLYWpWE0BPouwSjxXh7gKKNuWWcquwDMnJyXZ9Tevmm2+mYsWK9OzZEz8/v2yv9enTx6HFOUpCQgJhYWFcOAzBwe6uRhwuFqtHkcQ6k8nA5P9G0bTVNtres5GAErnsDGMp8GebboDKA6HfX/DKVgi6UoiCxeMlmCEkAeLj4wn20J2ssks8SizKLTsot8TR7Mktu88sHT16lK+//pobbrihwAWKiBuZDfj4pLNp/a3s3NaYRs1i8g6fgmwC8DfB5OawsB70/lvhI+6l7BLxYsotcSO7bx3eu3dv5s6d64xaRMRVDGAy+ZBwPoRN62/lg7cHs3bl3aRc9nfcNsyQ5gPHgjLCp/6TMPI2SPTLf1ERR1N2iXg55Za4iU1nliZMmGD577JlyzJv3jzWrVtH27Zts526GjVqlOMrFBGnyho+2Y7Y4bgjdlnDR0fsxFWUXSJFk3JLXMmmwVJsbGy25/fddx8A//77L//++6/DixIR17OEz4bW/B1TnyGPvZ9rv4aCygyfKc3hq1qw9xNrrfhEHEPZJVK0KbfEFWwaLM2ePdvZdYiImxmN6ZQOTqJR8xjatt+I4YTjt+GbDpWToc8eGL1VgSPOpewSKdqUW+IKdt/g4c8//6R58+YYsrTv/eOPP1i1ahXjxo1zaHEi4nzXh40jvzCb6fqw0WUM4mrKLpGiQ7klrmT3YOnOO+8kLi4u2/XelStX5sMPP1TgiHgLswvCxqCwEc+h7BLxcsotcRO7B0tmsznbkTmAnTt3emxvDRHJLh0zJ9N88Gv7O/+5byPlS1oPm3SgciPoVwpeibMvMAzAFSNERV8Lm3QDVHzGM3pYmMwGJidG0dRvmxrvFRPKLhHvpdxSbrmTzU1pS5cunSNoMgUGBjJz5kweeughhxbnKGrsV8TFouZ+dkgzQ/VEOGWGygbo7QevlICg6/6802KheiCc8oHKFwt/J6A0A1TvD6dKOmZ9hWEyG5ma+DzJ5kBKG5Jo5Bej8CkET25Kq+wSjxSLcssOyi3llqM5pSnt7t27MZvNNGzYkE2bNllWbDQaqVy5Mr6+dp+kEhE3SgOOmWFyKiy8kkf4OPL2qWbPuh2rCR8SzCFsSr2VnVcaK3yKIGWXSNGh3FJuuYPNZ5YylS9fntjYWIKCgpxVk8Pp6FwRF4uO0Nkh8wjd8ev+8n3JfsSu5OGMI3THr+v355tesCNsaQao3g+OX7frKOj6CiPzCF2iOfsOwUi6jtgVgCefWcqk7BKPEotyyw7KLeWWo9mTW0Z7V75+/XoCAgIKXJyIeKbMI3ZTUqF5EuR2FCVrz4nmvXOfz+btOnh9hXHtiF1r5iQ9g9mdxYhDKbtEih7llnLLFeweLHXt2pULFy5w8uTJbNPnz59P27Zt+e233xxWnIi4ji9Q1QAv+UN06dx7SfimQ9UEeCkaohcWvueEo9dXGEbSCTZcoLX/JgaWnk0uX3URL6TsEil6lFvKLVew+2Ltxx57jFatWnHq1CnKly/PtGnT6N69O//973958cUXee211/jpp5+cUauIOEHmZQx9/GB0lmu/066fz8G3U/Wk27PqMoaiT9klUnQot5RbrmT3YOnbb79l/PjxPPjgg2zZsoUePXrQrl07zp07x2OPPcZbb73ljDpFxMFyC5sc8zkyHDysh4XCpvhQdol4P+WWcssd7L4MLyEhgRtuuAF/f3+qVq1Keno6p0+fJiQkBMjoZWGLs2fPUr9+fQ4fPmzT/D169CAwMNDy6NSpk72liwhXe0kAUf6wOwgmlLQeOAbgigGiDsDuT2HCb4ULiKw9LByxvsIxk44Prf1/5z9BM2lfcp0Cp4hzRHYpt0TcQ7kFyi33sfvM0sSJE3nooYcICgri/Pnz1K5dm1deeQWz2cyHH35IZGRkvus4c+YMDz/8sM2BA7B9+3a2bt1KeHg4AH5+fvaWLlJkpJuhciL0y+W2qXlJSoPTZpiUCvf7Qlu/3NdnNuT/xdVUI5QYCi3j4auVUOWi9eZ5BpOBl9/NmOZfYiO4cSdvNJh5Ofhdt21fXK+w2aXcEikc5VbhKLfcx+4zSz179mTv3r188cUX7Nq1i9WrV9OlSxe2bNnC4cOHeeONN/Jdx5NPPkmPHj1s3mZcXBxms5kGDRpQpkwZypQpQ2BgoL2lixQZZsCfjF4T9RNh5CVItPEOOJkHxdKA2y+C3wXYeCXn+hIAfxNMrgn1n4SRt0GilX/rmQCDGbaEwQ0D4JZecLyUAR/S2ZR6Kx8kDmbtpbtJMftbnSbiCoXNLuWWSOEot8Rb2d1nyREOHTpEjRo1CAwMZPfu3VSvXj3P+VesWMHQoUPx9/fn/Pnz3H///UydOpWyZcvatD31qijiYil2/Squ7zlxfa+JvI7YnbkCFS/mvX5fIDQdLgBJPlen5dJX4rIRAp8DU+ahFzP4pBt5ecrzlLh4tQEo6QSSRAolSCXAMk3XXRcN3tBnqbBcnVug7CrSYlFuodwS93Fqn6VNmzbRoUMHGjZsSIMGDbI9bFWjRg27trl//36aNm3KihUr2LhxI4cPH2bs2LG5zp+SkkJCQoLlkZiYaNf2RLxN1q7m9h6xy219cT6QlGUPkbWDeV5H7DBkXAaR6nNtkgkfEgkhFf9s0zK7kOuInThbYbPL2bkFyi4pXpRb4i3sPrNUv359WrVqRZ8+ffD3z/4L0rZtW7s2busRuutt3LiRPn365Hrt+FtvvcX48eNzTNfRuSIqlmJ/hO56fkCEEfaWJkfPBVuO0NnCLx0iEmDHp1A66xE6wJhu5PmpzxOcaPsfnJE0yhgvMKT0++oT4WW84cySo7LLWbkFyq5iJRbl1nWUW+JK9uSW3Td4uHDhAmPHjrU7KBwpJCSEM2fOkJKSYrUje1RUFM8995zleWJiIrVr13ZliSJucf1tVQu98zaTo9ve9bdQtW8TOVd4/WUNChxxBndnV365BcouKZ6UW+Lp7L4M77nnnuOtt97iyhXX3Tuxd+/ebN261fJ827ZthIaG5ho4AQEBBAcHWx5BQUGuKlXELTK7mOd3W1V71heeDqVNWaZd7Vie7y1UzRlfnPVPvzbJSDpBXMCf1GzTMrqO6zao4nyuzi57cwuUXVK8KLfEW9h9ZunQoUP8+OOP1KtXj1atWmXbmX/44YeFKiYhIYGSJUvmuL1qw4YNGTFiBO+88w5nzpzh9ddfZ+DAgYXalkhRYGuDvvzWsaYk9L4Mp8zX1jf8JDQMhMvY2IzPDEYztDwBS7+BJZcgOcvRt9sCfmVW0mDSzL76gqy4nLOyS7klYh/llngbuwdL1atXd9oOv1WrVrzzzjt06dIl2/SoqCiOHDlCly5dqFixIgMGDCAqKsopNYh4g6wN+uwNm8x/0vkCP5W61q/iyuXs60vnWnO/0WtyDxsjGV+MvSUelln6VVxrnpcZLCazIcc0EVdxVnYpt0Rso9wSb+WWW4e7mm6/WsTF4pVflC1Mgz5bpZqgRCK0NMJXgVAllwtvrdWSGgslykDLf+GrLzPCJN0AlQdCv7+y34bVU1hrKijO5w03ePBGyq4iLBblVi6UW8otV3DqrcOvXLnCu+++yx133EHNmjXZvXs3t99+O4cOHSpwwSLFUWEa9NnKRMbRvC0muCERbkmE46ac81mr5ULmsuWuNe2LK3W14V9+t2F1G+tNBUWUXSKFp9xyBuWWp7N7sPT888+zfPlynnjiCZKSkihVqhQtW7ZkyJAhzqhPpMhzdK+J3JjIP3yy1tI0OGMZDBm3V90SBhH94XRJO3pXuIl6Ycj1lF0ijqPccjzlluey+zK88PBwfvvtNyIiIggPD2fz5s2YTCZatmzJyZMnnVVnoehShiIuFq+8nCG3nhP2dDXPz2UTBCZeDY/rGIEWVy9zqGTIpf+FlVuw5npbVitd0l3NZDYyNfF5Es3Z/9DVdd25vOEyPGWXeJRYlFu5UG5lUG45l1MvwwsPD+fXX3/NNu3gwYNu7bskUpRkHiWbkgrNk8DspG8VZh6xi0gEk7VUAuvNKKxMyzxiN6U5NO+dkUue5NoRu9bMSXrGaZ+peC5ll4jzKLccT7nlOey+G96bb77JI488wkcffcTFixcZPXo0v/32G3PmzHFGfSLFjsMb9OXCSMYXaJcFgjG3bdhzhK7ADf+cTw0ERdkl4jzKLcdTbnkOuwdL9957L9HR0SxdupTGjRsTHh7OG2+8QUREhBPKEyk+HNF7whZZwybzLkNp1i6pSIdjWc89X23a52uGKz5X50u3sZeFm+gyBsmk7BJxPOWW4ym3PI/dgyWAGjVqMHz4cEfXIlIsuTNs8qrlxZNQuQyYsjTtW7wKWj8Gp0oqbMT7KLtEHEO55XjKLc9VoMGSiGQoTM8Jaw36LqWDIQnCgPWlobaP9Z4Ttk4zmzOu8c66vnQzVEzIXrPJDPHm69ZHxpULt/wLy7L0q7hihKhozwwbyNlUUERErlFuOeFDLRTllqdTU1rxfrG47a5CmXcGOmV2zJ2AktIhKOna8zBgdSloejEjPDLvBPR5SaiZnH3aopJQ67ppS0pB6+Ts9Y0IgIZJ+U975SQExeKVd2wS1/KGu+F5I2VXERaLcku5JW5kT27ZNFiqX78+Bhu+WbZr1y7bq3QhBU4RF4vbQyfz1qWFvX3q9aGTGyM5b6tqIOfdfIyADxlHArPWd94MSVmmhZLRzC/rtMrp0Ps0vPIHBB2w731I8eKpgyVll3isWJRbKLfEfezJLZsuw5s9e7ZDChMp6rI2x1t4xTE9J6yxdsdUa0c9TNfNm1kf102LI+e0Yz4wuRIsvBt6h7q3D4VIQSi7RPKn3BLJmy7DE+8Xi8ccobueHxBhhL2lbbuVqq1H6FzNLx0iEmDvJ553e1VxP089s+TtlF1FWCzKLSdTbklenNqUVkTy5wtUNcBL/hBtY+B4Il8TVL0IL0VD9EIFjohIUaXcErFOd8MTcSBX3k7VFdd+9zkNo3Xtt4hIkaXcEsmbziyJOEDmEbkof9gdBBNKOiZwwoAdpa79oRqBW4xwINC2aYdKQ0VD9vpiSkPwddN2BuWctjsBJsRDUFrh34eIiHgW5ZaIbXRmSaQQrPWcKIyrDcZz9KswkxEiy7L0prBlWro5Z322ThMRkaJHuSViHw2WRArBxwCnCvjFa2uNAf2NUMEAT/hB2NXDbf5GMIXk3G55A7TzvRYQ1uaDjCAyX7fs9TVbm5YOVG4E/UrBK3G6o5CISFGg3BKxjy7DE3ETM+BPxu1a6yfCyEsZd2e5flqilTsWWVu2MPPlWp8JJteE+k/CyNsg0a9g71VERLyfckuKIw2WRNwsa4+LRokZwZN1Wl5h4ej5rNbnA8eCYHJzhY+IiCi3pHjRYEnEQ2Q22Uu6bprCR0REPJFyS4oDDZZEvEBmWExJheZJYM4lKBw9n9Vlr4bPlObQvLf1DuwiIlK8KbekqNBgScQL2Nos0NHzWV02HaomqOGfiIjkTrklRYXuhifiIXJtsmelWWCaOeeyhZnPpvrSoXIy9NkDo7fqLkMiIsWdckuKA5sGS/Xr18dgwxB+165dhS5IpLjJGgTDA6BhElw22xYOtoaIwkaKI2WXiHMot6Q4sWmwNHv2bGfXIVLsWGsMaGuTPVubCham+aABuGKAqAMweo3CRryPskvEsZRbUhzZNFhq27ats+sQKZR0M1ROyN4oz5NYa+QHtjXes8bR81ldFjj1FxBLRnKJeBlll3i63LLBEyi3RDLoBg9SJBSmiZ0rFKaRn4iIFE2enF3KLZEMDhsspaamOmpVIgVWmH4MrpC1vsZJ9jXyExHHU3aJJ/DkHFBuSXFn993w4uPjeeedd9i/fz/p6emW6Xv37uWff/5xaHEiBZV1R77wCvT2sEscMuuzNs1TaxbxZsou8QaenAPKLSmu7D6z1K9fP06dOkXJkiUpWbIkDz74IPv27WPAgAHOqE+kUArTxM5dvLFmEU+n7BJv4m054G31itjD7sFSdHQ0U6ZMYdiwYSQkJDBgwAA++OAD1qxZ44z6RAqlME3s3MUbaxbxdMou8SbelgPeVq+IPey+DC8sLIyff/6Zbt268ffff3Pp0iUaNGigPhXiUQrTn8EVMus7b86/kZ+IFJ6yS7yBJ+eAckuKK7sHS+PGjaNv377cfffddO7cmZYtWwJwyy23OLw4EXt5+o67MI38RKTglF3iyTw5u5RbUtzZfRlet27dOHDgAOXKlWPGjBmMGDGCIUOG8Nlnn9m1nrNnz1K/fn0OHz5s0/wbN26kWbNmVKtWjenTp9tbthRxWZvY7Q6CCSVt33mnmsB4AW5JhOOmws+XboaKCdnvDmStvmBDzmmlyLmstfWJiH0ckV3KLXG0gmaXckvEdQp06/CyZcvi4+ODn58fffr04ZlnniEoKMjm5c+cOUOPHj1sDpzTp0/Ts2dPHn74YX766ScWL17Mhg0bClK6FFGZTezsGSRlMpERCltMcENi7qFi63zWelNcJGd91mpWXwsR5ylMdim3xBkKml3KLRHXsXuwNGXKFK5cyd4Wef369XTo0MHmdTz55JP06NHD5vkXL15M5cqVGTlyJDVr1mTUqFHMnz/f5uVFbGUi/1Cxdb7C9KHIumyjRPW1ECmswmaXcks8lXJLxLnsHiyNGTOGS5cuZZtWt25d/vzzT5vXMWPGDAYPHmzz/Dt37qRdu3YYrt5epXnz5uzYsSPX+VNSUkhISLA8EhMTbd6WCHhW+MRx7cu0hV2fSHFV2Oxydm6BsksKR7kl4hw23+Dh119/BcBsNrNp0yYCAwMtz9esWUPNmjVt3miNGjXsKjIxMZG6detangcHB3P8+PFc5580aRLjx4+3axsi1mSGSkQinA+0bb6k0tbnydqH4qs02FvI26s6en0iRZGjssvZuQXKLnEM5ZaIY9k8WBo4cCAABoOBYcOGYTRmnJQyGo1ERkYyd+5c51QI+Pr6EhAQYHleokQJLl68mOv8UVFRPPfcc5bniYmJ1K5d22n1SdFlBFoaYVlg3qdhs82Xy47/+rsdFTYgHL0+kaLIXdllb26BskscQ7kl4lg2D5Z2794NQOnSpdm6dSvBwcFOK+p6ZcuW5cyZM5bniYmJ+Pv75zp/QEBAtpASsVfWEKlyNW0uW7lUwdp8adddWlCYW8L6AqHABdTXQqQg3JVd9uYWKLukcJRbIs5hd5+l9u3b4+fn54xactWsWTOWLl1qeR4TE0OVKlVcWoMUD9ZCpKDzFTZs1NdCxHFcnV3KLXEV5ZaIc9l9g4fly5dTsmRJAFJTUzGbzZhMedy83w4JCQk57lYE0KlTJzZt2sSGDRtIS0tj2rRptG/f3iHbFDGScevTW4xwNAg2BVkPElvnK0zPJ1v7WihwROzjrOxSbok7KLdEXMfuwVJiYiJDhgwhIiKCChUq8Ndff1GrVi22b99e6GJatWrF6tWrc0yvUKECEyZMoFu3bkRGRrJ7926GDx9e6O2JAPgbwRSSe4jYO19hej5ZW7Yw6xORDM7KLuWWuINyS8R1DMnJyXbdvLF3795cvHiR//znPzzxxBNs2bKFxYsX8+2337J+/XonlZnh4MGD7N27lzZt2th13XlCQgJhYWFcOAwu/KqVuEpsloc4Tiz6bMUmCWYISYD4+HiXfp/VHu7KroLmFii7irRYtG91hlj02YpN7Mktu7+z9PPPP/PHH38QHh6O0WjEYDDw2GOPMWnSpAIXbKvIyEgiIyOdvh0RESla3JVdyi0REe9m92V4tWvXZuHChUDGrVgNBgNbtmyhXr16Di9ORETEEZRdIiJSEHafWZo8eTIPPvggc+bMITExkSeeeIIjR45ku+uPiIiIJ1F2iYhIQdg9WGrevDkxMTF89913xMfHEx4eTocOHQgJCXFGfSIiIoWm7BIRkYKwe7AEEBISQq9evTh79iwVKlTAoBbMIiLi4ZRdIiJiL7u/s3Ty5El69epFuXLluPHGGylXrhxPPPEEp06dckZ9IiIihabsEhGRgrB7sDRo0CAANmzYwD///MO6deu4fPmyZbqIiIinUXaJiEhB2H0Z3pYtW9i8eTPVqlUDoGLFirz77ru0bt3a4cWJiIg4grJLREQKwu4zS23btmXZsmXZpi1ZsoS7777bYUWJiIg4krJLREQKwu4zSydOnOC1115j9uzZhIeHc/ToUeLj42nZsiUdO3YE4Pvvv3d4oSIiIgWl7BIRkYKwe7A0YMAABgwY4IxaREREnELZJSIiBWH3YKlPnz7OqENERMRplF0iIlIQdn9nSUREREREpDjQYElERERERMQKDZZERERERESs0GBJRERERETECg2WRERERERErNBgSURERERExAoNlkRERERERKzQYElERERERMQKDZZERERERESs0GBJRERERETECg2WRERERERErNBgSURERERExAoNlrxMerqRijVPMXLseBITS7u7HBERkXwpu0TEW2mw5GXMZgP+fqlMnhlF/Vt2K3hERMTjKbtExFtpsOSNDGbS0vw4dvwGBY+IiHgHZZeIeCENlrycgkdERLyNsktEvIUGS0VEZvBM+eAlmt8Rjdns7opERETypuwSEU+nwVIR4eubStUqR3hp8GSi1zfHYHB3RSIiInlTdomIp/N1dwFSOL6+qVSudII+PRcy+sXxBAUlubskERGRPCm7RMRbaLDkjcwGBY2IiHgXZZeIeCENlryMwWDmyhU/ooZMVtCIiIhXUHaJiLdyy3eWdu3aRdu2bQkPD2f06NGYbfhGZ48ePQgMDLQ8OnXq5IJKPY+Pj4lTB0KZMGa0JWzU7E9ExPmUXQWn7BIRb+XywVJKSgoPP/wwTZs2ZePGjezZs4cFCxbku9z27dvZunUrcXFxxMXFsWTJEhdU6x3U7E9ExLmUXY6n7BIRb+DywdKPP/5IQkICEydO5MYbb2Ts2LF8+umneS4TFxeH2WymQYMGlClThjJlyhAYGOiiir2Emv2JiDiNsstJlF0i4uFcPljauXMnLVq0oFSpUgA0atSIPXv25LnMn3/+SXp6OrVq1aJixYo8+eSTnDt3Ltf5U1JSSEhIsDwSExMd+h48nYJHRMSxlF3Op+wSEU/k8sFSQkICERERlucGgwEfH588A2T//v00bdqUFStWsHHjRg4fPszYsWNznX/SpEmEhYVZHrVr13bgO/AeavYnIuIYyi7XUXaJiCdx+WDJ19cXf3//bNMCAgK4dOlSrstERUWxfPly6tevT926dXnjjTdYvnx5nvPHx8dbHvv27XNU+V5Fzf5ERBxD2eU6yi4R8SQuv3V42bJl2b17d7ZpSUlJ+Pn52byOkJAQzpw5Q0pKCgEBATleDwgIsDq9uFAfCxERx1J2OZ+yS0Q8kcvPLDVv3pw//vjD8vzw4cOkpKRQrly5XJfp3bs3W7dutTzftm0boaGhxTpUcrja7K9qlSNEDZnM7s0Nst2iVURECk7Z5STKLhHxcC4fLLVp04YLFy6wcOFCACZPnsydd96Jj48PCQkJXLlyJccyDRs2ZMSIEfzxxx98//33vP766wwcONDVpTuMrb0lLl3yx1DWRJW6x9h3IDLXZU0mA/Enw2je5E+2rG2Va9Cop4WISMEou5RdIlI8ueU7SzNmzGDYsGFERESwfPlyXn/9dQBatWrF6tWrcywTFRVF3bp16dKlC8OHD2fAgAFERUW5unSHsbW3RHq6DwDxJ6tQp8V+qtQ9xt79NXMsm3wxkLDQeKJ3tKBV+625rk89LURECkbZpewSkeLJkJyc7Jb7zBw/fpxt27bRqlUrKlas6NRtJSQkEBYWxoXDEBzs1E3ZJC3Nh+qNYzkeXxUAX98rVK50gt4Pf8YrL127TjspqSRBNyQDmd9uzfhRGY3pmEy+2ZY9fyGEpOTgPNdn63a9TmyWhzhOLPpsxSYJZghJgPj4eII9YSfrRMouZZdDxKJ9qzPEos9WbGJPbrltsORKnh44ma4PAIMh/brAyWS2adr16ytZ8pJN2/W64IlFO0ZniEWfrdikOA2WXEnZVYSzKxbtW50hFn22YhN7csvll+FJ7mzvLWHtPqo5p9m6PvW0EBGRglJ2iUhRpsGSB7G9t4S1RMg5zdb1qaeFiIgUlLJLRIoyl/dZkpxy6y2RlFTyujmtXfedSuVKJ6+77tv6+tLSfGzaroiISH6UXSJSHGiw5C5Xe0vYtsPPCJqwynGs/fpu7nngJ06dqWhZdviwt2nYeheXU0rkvz67tisiIpKFsktEihkNltzAYDBz5YofUUMm57nD9/FJBzKCZv3KO6hd8yDp6cYcy1qbVpjtioiIXE/ZJSLFkQZLbpCebuTMvxX4eeMdPDdwOkFBSVy65E+pKpcJCz3O+lXtqF3zIP7+aVQof4Ynei0gLPQkAD4+Jk4dCM2xTjOGfL/cmtuyIiIi+VF2iUhxpBs8uIHJZMRgMLMl+hZuaHiUW9r/zrG4cCD/Jn5q2CciIu6g7BKR4kh9ltzg8mV/AsOTLV90BTMGQzpmsw+2NPErFg377BGLeio4Qyz6bMUm6rPkHMquIpxdsWjf6gyx6LMVm6jPktcxYDZff0WkATBgMl27C1BmT4n8jsLZOp+IiEjBKbtEpOjTYMmjqGGfiIh4G2WXiBRdGix5FDXsExERb6PsEpGiS3fD8whZr/u+Ng2sNfFTwz4REfEEyi4RKfp0ZsltDIAZozGNW27exN6tta9ONwNmwirHsWtTXSpXOmk5yhY1ZDK7NzdgwpjROUPkasO+fOcTEREpMGWXiBQvOrPkBkajCbPZwC03b2LZpw9RJewEly75A/k38bNGDftERMTZlF0iUhzpzFIBpacbqVjzVL5360lN9cVYLp1b2v/O8fjKlmlms4Gduxty4lQlAC5cKAVA/Ikq/Lm9KQD//lua02crMnHqcNZuaGeZz1DWhF/Fy2zcdItlO7Y09rO1ZhERKZqUXSIi9tFgqYBsbaZnrYnfkWMZPSWSLwbR/I7/o3T4Bf7c3uzqEgZ6D1yCoWw6i5b1uDrNyIOPr8RQNo2vVnUBIC3Nn9vv/z0jeH5vrQaAIiKSL2WXiIh91JS2gGxtpmetiR+kAzmb+GUo6DQzmWPfYtcAMBY1oHOGWPTZik3UlNY5lF1FOLti0b7VGWLRZys2UVNaN7C9mZ6BnF8VM5CzJ4W9065NVwNAERGxhbJLRCRvOrNUQNcf6bqen18qETfEsuPXRpSumvXonOtl1rJrc30imuRf894/63hXX4tYdBTJGWLRZys20Zkl51B2FeHsikX7VmeIRZ+t2ERnltzI9mZ6tjXxK8w0NQAUERFbKLtERKzTrcMdJLdmepcv+183Z9brvrNOu56908xkXs6gBoAiImILZZeISN50ZqkwbG6ml72J365N9TJXAJgJDExk5aL22aaBmakT+l83zcRHMx7NNs3XN5Wfv2lLlcrxagAoIiL5U3aJiNhMg6UCytpML3OnXarUxRy9ILI28Tv61w1sWnMb1aoeAyAwMJHo9TeRdCyExg1irq7ZzIx3BmI+50OPLsst0+ZOfwLzOV8e7LwSyDiy9st3t3LldAna3ropRy3WAsRazQoaEZHiQ9klImIf3eDBgdLSfKje6DCnzlSy+7amGbdpvYjJZMRoTKdF0z/4fO4j1Gz+T7ZpXy14kCphJ5z3JrxRLPoypzPEos9WbKIbPDiHsqsIi0X7VmeIRZ+t2EQ3eHAng7kQtzXNuHbbZPJlS/Qt1Gx+EJPJkG1aZnPAzI7qIiIihabsEhGxSoMlJypcLwjD1Vu2GnJMU/CIiIizKLtERK7RYMkFMoNnygcv0fyOaMx2Xfho7V6o14InovFhTCYHFSoiInKVsktERIMllyhcLwjrt1zNvDtRbEx1jPopioiIgym7RETUZ8mpCtcLwozRmI7JZOTaEbqMaS2bbWXZpw/py7IiIuJwyi4RkWs0WHK0q70gChY0mT0tMkJl0f8evXpHIQWNiIg4kbJLRMQqDZYcKGsvCHuPxmXtaZEZKqmpvjmmiYiIOJKyS0Qkd265YnjXrl20bduW8PBwRo8ejdmGb41u3LiRZs2aUa1aNaZPn+6CKu3n42Pi1IHQAjXM8/dPw/SvD5vW3GYJFmvTRETEPZRdOSm7RKSoc/lgKSUlhYcffpimTZuyceNG9uzZw4IFC/Jc5vTp0/Ts2ZOHH36Yn376icWLF7NhwwYXVSwiIsWdsktEpHhy+WDpxx9/JCEhgYkTJ3LjjTcyduxYPv300zyXWbx4MZUrV2bkyJHUrFmTUaNGMX/+fBdVLCIixZ2yS0SkeHL5YGnnzp20aNGCUqVKAdCoUSP27NmT7zLt2rXDcPW+pc2bN2fHjh25zp+SkkJCQoLlkZiY6Lg3ICIixY6yS0SkeHL5YCkhIYGIiAjLc4PBgI+PD+fOnct1mcTERKpXr255HhwczPHjx3Odf9KkSYSFhVketWvXdkjtIiJSPCm7RESKJ5cPlnx9ffH39882LSAggEuXLuW5TEBAgOV5iRIluHjxYq7zR0VFER8fb3ns27ev8IWLiEixpewSESmeXH7r8LJly7J79+5s05KSkvDz88tzmTNnzlieJyYm5gitrAICArIFlIiISGEou0REiieXn1lq3rw5f/zxh+X54cOHSUlJoVy5crku06xZM7Zu3Wp5HhMTQ5UqVZxap4iISCZll4hI8eTywVKbNm24cOECCxcuBGDy5Mnceeed+Pj4kJCQwJUrV3Is06lTJzZt2sSGDRtIS0tj2rRptG/f3tWli4hIMaXsEhEpntzynaUZM2YwbNgwIiIiWL58Oa+//joArVq1YvXq1TmWqVChAhMmTKBbt25ERkaye/duhg8f7urSRUSkmFJ2iYgUT4bk5OT8W5A7wfHjx9m2bRutWrWiYsWKNi1z8OBB9u7dS5s2bQgODrZ5WwkJCYSFhXHhMNixmHiL2CwPcZxY9NmKTRLMEJIA8fHxdu2bvZGySxwiFu1bnSEWfbZiE3tyy+U3eMhUpUoVu6/djoyMJDIy0kkViYiI5E3ZJSJSvLj8MjwRERERERFvoMGSiIiIiIiIFRosiYiIiIiIWKHBkoiIiIiIiBUaLImIiIiIiFihwZKIiIiIiIgVGiyJiIiIiIhYocGSiIiIiIiIFRosiYiIiIiIWKHBkoiIiIiIiBUaLImIiIiIiFihwZKIiIiIiIgVGiyJiIiIiIhYocGSiIiIiIiIFRosiYiIiIiIWKHBkoiIiIiIiBUaLImIiIiIiFihwZKIiIiIiIgVGiyJiIiIiIhYocGSiIiIiIiIFRosiYiIiIiIWKHBkoiIiIiIiBUaLImIiIiIiFihwZKIiIiIiIgVGiyJiIiIiIhYocGSiIiIiIiIFb7uLsAVzGYzAAmJbi5EnCMJuAhcdnchRUwKcAVIB8xurkU8WsLV34/Mfa04hrKrCFNuOYdyS2xkT24Vi8FSUlISADc0dHMhIiJFWFJSEiEhIe4uo8hQdomIOJctuWVITk4u8mNvk8lEfHw8pUuXxmAwOGUbiYmJ1K5dm3379hEUFOSUbbiC3odn0fvwLHof1pnNZpKSkggLC8No1NXdjqLssp3eh+coCu8B9D48jTtzq1icWTIajYSHh7tkW0FBQQQHB7tkW86k9+FZ9D48i95HTjqj5HjKLvvpfXiOovAeQO/D07gjt3QIUERERERExAoNlkRERERERKzQYMlBAgICGD16NAEBAe4upVD0PjyL3odn0fuQoqao/C7ofXiOovAeQO/D07jzfRSLGzyIiIiIiIjYS2eWRERERERErNBgSURERERExAoNlqRIOnv2LJs3b+bMmTPuLkVERMQmyi4Rz6PBkoOcPXuW+vXrc/jwYXeXUmCrVq2iQYMGBAcH065dO/bs2ePukgpk6dKlNG7cmBdeeIG6deuydOlSd5dUKN26dWPBggXuLqPAXnrpJQIDAy2PRo0aubukQnnttdfo0aOHu8sokAULFmT7WWQ+vPn3SwpOueVZlF2eQ7nlOTwhtzRYcoAzZ87Qo0cPrw6cf/75h0GDBvH666+zf/9+qlWrxuDBg91dlt3Onz9PVFQUP/74I5s2bWL69Om89tpr7i6rwL744gvWrl3r7jIKZfv27Sxbtoy4uDji4uL4/fff3V1Sge3atYv//e9/vPPOO+4upUB69epl+TnExcWxb98+KlSoQJs2bdxdmriYcsuzKLs8i3LLc3hCbvm6bEtF2JNPPkmPHj3YunWru0spsL179zJ27FgeeughAPr370/37t3dW1QBJCUl8fbbb9OgQQMAGjVqxPnz591bVAH9+++/jBo1itq1a7u7lAJLS0tj9+7dtGnThtKlS7u7nEIxm80MHTqUwYMHc+ONN7q7nALx9/fH39/f8vx///sfXbt2pUaNGm6sStxBueVZlF2eQ7nlWTwht3RmyQFmzJjhtUezMnXs2JH+/ftbnu/fv5/IyEg3VlQwVatW5ZFHHgHgypUrTJs2ja5du7q5qoIZNWoUXbt2pUWLFu4upcB27tyJ2WymdevWlC9fnm7dunH06FF3l1UgH3/8MTExMVSvXp3vv/+eK1euuLukQrl8+TIzZ84kKirK3aWIGyi3PIuyy3MotzyXu3JLgyUHKGpHZVNTU5k2bRoDBgxwdykFFhMTQ40aNVi3bp1XnnresGED69ev54033nB3KYWyd+9e6tWrxyeffEJ0dDR+fn4899xz7i7LbklJSYwbN47IyEiOHz/O9OnTuffee7l8+bK7SyuwxYsX07JlS6pXr+7uUsQNlFueSdnlfsotz+Wu3NJgSXIYN24cgYGB9O3b192lFFijRo1YtWoV9erVY9CgQe4uxy6XL19m6NChTJs2jeDgYHeXUyiPPPII69evp3nz5kRERDBlyhTWrVtHQkKCu0uzy4oVK7h48SLfffcdI0eOZOXKlVy4cIFFixa5u7QCmzdvHv369XN3GSIOURRyC5RdnkC55bnclVv6zpJks27dOubNm8fPP/+Mn5+fu8spMIPBwE033cScOXOoW7cu586do2zZsu4uyyYTJ06kWbNmdOjQwd2lOFxISAgmk4kTJ054VZgeP36cm2++mXLlygHg6+tLw4YNiY2NdW9hBXTw4EH++ecf7rrrLneXIlJoRSW3QNnliZRbnsGduaUzS2Jx6NAh+vbty9SpU6lXr567yymQ9evXM3r0aMtzHx8fAIxG7/lVX7JkCd9++y1VqlShSpUqLFmyYWsMVwAABvVJREFUhBdeeIHnn3/e3aXZbcSIESxbtszyPDo6GqPRSNWqVd1Ylf3Cw8NzXLpw5MgRqlWr5qaKCuerr76iQ4cOXv8PS5GikFug7PIkyi3P5M7c0pklAeDSpUv06NGDLl260LlzZ5KSkgAIDAzEYDC4uTrb1alTh0ceeYSaNWty7733Mm7cOO6++25CQkLcXZrN1qxZQ1pamuX56NGjadGiBX369HFjVQXTuHFjxo0bR2hoKGlpaURFRdGnTx9KlSrl7tLs0qFDB6Kiopg7dy4dO3ZkxYoVxMTEcM8997i7tAJZs2aNV/4+iWRVVHILlF2eRLnlmdyZW95zyEKcau3atezZs4ePP/6Y0NBQy+PIkSPuLs0uYWFhLFiwgJkzZ3LzzTdz6dIl5s6d6+6y7BIeHk716tUtj8DAQMqXL0+FChXcXZrdevfuTffu3enVqxfPPPMMd911F5MmTXJ3WXYrV64cy5cv5/PPP6dJkybMnDmT+fPne+XNES5dusQff/xBq1at3F2KSKEUldwCZZcnUW55HnfnliE5Odnsli2LiIiIiIh4MJ1ZEhERERERsUKDJRERERERESs0WBIREREREbFCgyURERERERErNFgSERERERGxQoMlERERERERKzRYEnGDX375xeHd5m1Zp63bXbBgAR06dHBUaSIi4uWUW1JcabAkUgj16tXjl19+cXcZANx6661s2bLF7uU86T2IiIhzedI+X7kl3kCDJZEiwtfXl+DgYHeXISIiYhPllngDDZakWFuwYAG33347PXv2JCwsjG7duhEfH295PTo6mnbt2hEWFsajjz7KhQsXAOjWrRuBgYEcOXKEjh07EhgYyKRJkyzLrVixgiZNmlCxYkU6duzI8ePHC1yjyWSiUqVKHDp0iBkzZlC9enUA+vTpw/Tp0y3z5XapwieffEKtWrWIjIxkzZo1lun5vQeAjz/+mIiICKpXr87XX39d4PcgIiKOodxSbolrabAkxV50dDQtW7Zk06ZNBAQEMGzYMADOnz/PAw88QIcOHdi6dSsXL15k1KhRACxatIi4uDiqVq3Kl19+SVxcHM899xwA//77L0899RTDhw8nJiaGsmXL8vbbbxe4PqPRSMOGDdm/fz/79u2jVq1anD17lgMHDtCkSZM8l42JieHFF1/kvffeY8WKFXz11VeW1/J6DwB///03y5cvZ+3atfTp04eRI0cW+D2IiIjjKLeUW+I6vu4uQMTdwsPDeemllzAYDIwePZrbb7+dtLQ0vv/+e3x9fRk5ciQGg4EhQ4bQv39/AAIDA4GMQAgMDKRMmTKW9QUFBbFnzx6CgoLYtm0bqamp7N+/v1A1NmnShAMHDnD06FHuuOMO9u3bx6FDh/INnVWrVnHXXXfRuXNnAJ5//nmmTJmS73sASEpKYs6cOYSGhvLUU08xderUQr0HERFxDOWWcktcR4MlKfbCw8MxGAwAVKlShfT0dM6ePcvx48c5c+YM4eHhQMZlBYmJiVy+fJkSJUrkuj6z2cx///tfVq5cSd26dQkODsZkMhWqxiZNmhATE0NaWhq1atXil19+oWLFijmC4nonTpygatWqlucRERE2b7NOnTqEhoYC4O/vX5CyRUTECZRb1im3xBl0GZ4Ue0ePHrWEwrFjx/D19aVChQqEh4fTrFkzNm3axKZNm9i8eTO///47fn5+lmWNRiNmsznb+hYvXsyvv/7Kvn37+Omnn7j//vsLXWOTJk3YunUrQUFB1KxZk5UrV+Z7dA6gYsWK2a5lP3bsWI55rL0HQF+6FRHxUMot5Za4jgZLUuzFx8czadIkYmNjmTBhAp07d8bHx4cOHTpw5MgR/vzzT3x8fFi6dCndu3fPtoO+8cYbWbNmDfHx8fz8888AJCcnA3Du3Dl++OEHJk6caHWnbo/69evz119/ERkZSWRkJNu3b+emm27Kd7nOnTuzdu1aVq9eze7du61ekmDtPYiIiOdSbim3xHU0WJJir2XLluzYsYPWrVuTmprK5MmTAShTpgxLly5l+vTpNG3alJUrV7J06VJ8fa9dvTp+/HjWrFlDgwYNmDBhAgCPPfYYkZGRNGvWjAkTJtCvXz/27t3L5cuXC1xjQEAAderUoWbNmpQpU4YKFSrYdISuadOmTJw4kSFDhvDggw9yzz335JjH2nsQERHPpdxSbonrGJKTkwt36EDEiy1YsICFCxeyevVqd5ciIiKSL+WWiGvpzJKIiIiIiIgVOrMkIiIiIiJihc4siYiIiIiIWKHBkoiIiIiIiBUaLImIiIiIiFihwZKIiIiIiIgVGiyJiIiIiIhYocGSiIiIiIiIFRosiYiIiIiIWKHBkoiIiIiIiBUaLImIiIiIiFjx/8EE+YcB+z7kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import numpy as np\n",
    "import copy\n",
    "from matplotlib import pyplot as plt\n",
    "from collections import Counter\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "plt.rcParams[\"font.sans-serif\"] = \"SimHei\"\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False\n",
    "\n",
    "# prepare for display in different class\n",
    "ids_collection = [[],[],[]]\n",
    "for ids,(data,label) in enumerate(zip(X_ex,y_ex)):\n",
    "    if label == 0:\n",
    "        ids_collection[0].append(data)\n",
    "    elif label == 1:\n",
    "        ids_collection[1].append(data)\n",
    "    else:\n",
    "        ids_collection[2].append(data)    \n",
    "ids_collection = np.array(ids_collection,dtype=np.float64)\n",
    "\n",
    "# prepare for display of classification plane\n",
    "X_mesh = np.linspace(X_ex.min(axis=0)[0]-0.5,X_ex.max(axis=0)[0]+0.5,100)\n",
    "Y_mesh = np.linspace(X_ex.min(axis=0)[1]-0.5,X_ex.max(axis=0)[1]+0.5,100)\n",
    "XX_mesh, YY_mesh = np.meshgrid(X_mesh, Y_mesh)\n",
    "ZZ_mesh = np.zeros_like(XX_mesh)\n",
    "ZZ_mesh_collection = [[],[],[],[]]\n",
    "\n",
    "for ids,clf in enumerate([tree_ex_2_clf,tree_ex_3_clf,tree_ex_5_clf,tree_ex_12_clf]):\n",
    "    # predict each point in meshgrid\n",
    "    for idx in range(len(X_mesh)):\n",
    "        for idy in range(len(Y_mesh)):\n",
    "            ZZ_mesh[idx,idy] = clf.predict([[X_mesh[idx],Y_mesh[idy]]])\n",
    "    ZZ_mesh_collection[ids] = ZZ_mesh\n",
    "    ZZ_mesh = np.zeros_like(XX_mesh)\n",
    "\n",
    "fig, ax = plt.subplots(2,2,figsize=(10,8),\n",
    "                      facecolor=\"whitesmoke\",\n",
    "                      edgecolor=\"gray\")\n",
    "fig.suptitle(\"classification boundaries with different depth\")\n",
    "fig.subplots_adjust(top=0.92,hspace=0.20,wspace=0.25)\n",
    "for ai,dep,ZZ in zip(ax.flat,[2,3,5,12], ZZ_mesh_collection):\n",
    "    ai.contourf(XX_mesh,YY_mesh,ZZ,cmap=plt.cm.autumn_r)\n",
    "    ai.scatter(ids_collection[0,:,0],ids_collection[0,:,1],marker='>',\n",
    "               lw=.5,color='blue', label=\"class setosa\")\n",
    "    ai.scatter(ids_collection[1,:,0],ids_collection[1,:,1],marker='>',\n",
    "               lw=.5,color='green', label=\"class versicolor\")\n",
    "    ai.scatter(ids_collection[2,:,0],ids_collection[2,:,1],marker='>',\n",
    "               lw=.5,color='purple', label=\"class virginica\")\n",
    "    ai.legend(loc=\"best\")\n",
    "    ai.set_xlabel(\"petal width\")\n",
    "    ai.set_ylabel(\"petal length\")\n",
    "    ai.set_title(\"decision tree with \" + str(dep) + \"s depth\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e9eb6ce",
   "metadata": {},
   "source": [
    "## notice\n",
    "1. note that depth will determine the final probabilities of each class due to the fact that shallower trees would be more likely to experience early-stopping\n",
    "2. thus the leaf nodes of shallower trees would be less pure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "847244d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.         0.33333333 0.66666667]]\n",
      "[[0.         0.33333333 0.66666667]]\n",
      "[[0.         0.02173913 0.97826087]]\n"
     ]
    }
   ],
   "source": [
    "width_value = 4.5\n",
    "length_value = 2.5\n",
    "\n",
    "print(tree_ex_12_clf.predict_proba([[width_value,length_value]]))\n",
    "print(tree_ex_5_clf.predict_proba([[width_value,length_value]]))\n",
    "print(tree_ex_2_clf.predict_proba([[width_value,length_value]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "482739d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2]\n",
      "[2]\n",
      "[2]\n"
     ]
    }
   ],
   "source": [
    "print(tree_ex_12_clf.predict([[width_value,length_value]]))\n",
    "print(tree_ex_5_clf.predict([[width_value,length_value]]))\n",
    "print(tree_ex_2_clf.predict([[width_value,length_value]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "179168eb",
   "metadata": {},
   "source": [
    "# Mathmatical Background\n",
    "## Impurity Function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a80b4f09",
   "metadata": {},
   "source": [
    "### Gini Impurity\n",
    "$$\n",
    "    Gini(D) = 1-\\displaystyle\\sum_{k=1}^{N}{{p_k}^2}\n",
    "$$\n",
    "\n",
    "for property a, its Gini index should be like\n",
    "\n",
    "$$\n",
    "    Gini_{index}(D,a) = \\displaystyle\\sum_{v=1}^{V}{\\frac{|D^v|}{|D|}Gini(D^v)}\n",
    "$$\n",
    "\n",
    "in formula above, we have $V$ properties, its index is $(1,2,\\cdots,k,\\cdots,N)$, and $a$ is one specific property"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb2593f0",
   "metadata": {},
   "source": [
    "## Prune parameters\n",
    "1. min_samples_split\n",
    "2. min_samples_leaf\n",
    "3. min_weight_fraction_leaf\n",
    "4. max_leaf_nodes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7855955",
   "metadata": {},
   "source": [
    "# Decision Tree Regressor\n",
    "1. mainly used in numerical and continuous data\n",
    "2. instead of calculating impurity in each node, this tree calculate MSE, on which dividing point the model would produce smaller total MSE\n",
    "\n",
    "$$\n",
    "    J(v,a) = \\frac{|D_{left}|}{|D|}{MSE}_{left} + \\frac{|D_{right}|}{|D|}{MSE}_{right}\n",
    "$$\n",
    "\n",
    "where $v$ stands for vth attribute and $a$ stands for ath value in vth attribute, $|D|$ stands for numbers of total data points, and other parameters are list as follows:\n",
    "\n",
    "$$\n",
    "\\left \\{\n",
    "\\begin{array}{ll}\n",
    "MSE_{node} = \\displaystyle\\sum_{i \\in node}{(\\hat y_{node} - y_i)^2} \\\\\n",
    "\\hat y_{node} = \\frac{1}{m_{node}} \\sum_{i \\in node}{y_i}\n",
    "\\end{array}\n",
    "\\right.\n",
    "$$\n",
    "\n",
    "<hr>\n",
    "1. Decision Tree Regressor will perform like its classifier, classification plane will always parallel to axis, and regression result will simply be the <font color=maroon><b>average of all data in one leaf node.</b></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b492a818",
   "metadata": {},
   "source": [
    "## sklearn interpretation\n",
    "```doc\n",
    "DecisionTreeRegressor(\n",
    "    *,\n",
    "    criterion='squared_error',\n",
    "    splitter='best',\n",
    "    max_depth=None,\n",
    "    min_samples_split=2,\n",
    "    min_samples_leaf=1,\n",
    "    min_weight_fraction_leaf=0.0,\n",
    "    max_features=None,\n",
    "    random_state=None,\n",
    "    max_leaf_nodes=None,\n",
    "    min_impurity_decrease=0.0,\n",
    "    ccp_alpha=0.0,\n",
    ")\n",
    "\n",
    "criterion : {\"squared_error\", \"friedman_mse\", \"absolute_error\",             \"poisson\"}, default=\"squared_error\"\n",
    "    The function to measure the quality of a split. Supported criteria\n",
    "    are \"squared_error\" for the mean squared error, which is equal to\n",
    "    variance reduction as feature selection criterion and minimizes the L2\n",
    "    loss using the mean of each terminal node, \"friedman_mse\", which uses\n",
    "    mean squared error with Friedman's improvement score for potential\n",
    "    splits, \"absolute_error\" for the mean absolute error, which minimizes\n",
    "    the L1 loss using the median of each terminal node, and \"poisson\" which\n",
    "    uses reduction in Poisson deviance to find splits.\n",
    "\n",
    "splitter : {\"best\", \"random\"}, default=\"best\"\n",
    "    The strategy used to choose the split at each node. Supported\n",
    "    strategies are \"best\" to choose the best split and \"random\" to choose\n",
    "    the best random split.\n",
    "\n",
    "max_depth : int, default=None\n",
    "    The maximum depth of the tree. If None, then nodes are expanded until\n",
    "    all leaves are pure or until all leaves contain less than\n",
    "    min_samples_split samples.\n",
    "\n",
    "min_samples_split : int or float, default=2\n",
    "    The minimum number of samples required to split an internal node:\n",
    "\n",
    "min_samples_leaf : int or float, default=1\n",
    "    The minimum number of samples required to be at a leaf node.\n",
    "    A split point at any depth will only be considered if it leaves at\n",
    "    least min_samples_leaf training samples in each of the left and\n",
    "    right branches.  This may have the effect of smoothing the model,\n",
    "    especially in regression.\n",
    "\n",
    "min_weight_fraction_leaf : float, default=0.0\n",
    "    The minimum weighted fraction of the sum total of weights (of all\n",
    "    the input samples) required to be at a leaf node. Samples have\n",
    "    equal weight when sample_weight is not provided.\n",
    "\n",
    "max_features : int, float or {\"auto\", \"sqrt\", \"log2\"}, default=None\n",
    "    The number of features to consider when looking for the best split:\n",
    "\n",
    "random_state : int, RandomState instance or None, default=None\n",
    "    Controls the randomness of the estimator. The features are always\n",
    "    randomly permuted at each split, even if ``splitter`` is set to\n",
    "    ``\"best\"``. When ``max_features < n_features``, the algorithm will\n",
    "    select ``max_features`` at random at each split before finding the best\n",
    "    split among them. But the best found split may vary across different\n",
    "    runs, even if ``max_features=n_features``. That is the case, if the\n",
    "    improvement of the criterion is identical for several splits and one\n",
    "    split has to be selected at random. To obtain a deterministic behaviour\n",
    "    during fitting, ``random_state`` has to be fixed to an integer.\n",
    "    See :term:`Glossary <random_state>` for details.\n",
    "\n",
    "max_leaf_nodes : int, default=None\n",
    "    Grow a tree with max_leaf_nodes in best-first fashion.\n",
    "    Best nodes are defined as relative reduction in impurity.\n",
    "    If None then unlimited number of leaf nodes.\n",
    "\n",
    "min_impurity_decrease : float, default=0.0\n",
    "    A node will be split if this split induces a decrease of the impurity\n",
    "    greater than or equal to this value.\n",
    "\n",
    "ccp_alpha : non-negative float, default=0.0\n",
    "    Complexity parameter used for Minimal Cost-Complexity Pruning. The\n",
    "    subtree with the largest cost complexity that is smaller than\n",
    "    ccp_alpha will be chosen. By default, no pruning is performed. \n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7cb2ed5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "\n",
    "np.random.RandomState(42)\n",
    "X_reg = np.linspace(-1,1,100)\n",
    "y_reg = np.power(X_reg,2)\n",
    "y_reg_noise = y_reg + np.random.normal(loc=0, scale=.2, size=X_reg.shape[0])\n",
    "\n",
    "tree_reg = DecisionTreeRegressor(max_depth=8, min_samples_leaf=10)\n",
    "tree_reg.fit(X_reg.reshape(-1,1), y_reg_noise.reshape(-1,1))\n",
    "X_pred = tree_reg.predict(X_reg.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1291fba9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "fig, ax = plt.subplots(1,1,figsize=(6,8),\n",
    "                       facecolor=\"whitesmoke\",\n",
    "                      edgecolor=\"gray\")\n",
    "fig.suptitle(\"DT regressor on numeric continuous data\")\n",
    "fig.subplots_adjust(top=0.92)\n",
    "ax.plot(X_reg,y_reg, ls='-', lw=1, color=\"orange\", label=\"SS Regressor\")\n",
    "ax.plot(X_reg,X_pred, '-r', label=\"DT Regressor\")\n",
    "ax.scatter(X_reg,y_reg_noise, marker='*',lw=.5, color=\"blue\", label=\"raw\")\n",
    "ax.legend(loc=\"best\")\n",
    "ax.grid(alpha=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "610f54ae",
   "metadata": {},
   "source": [
    "# Ensemble\n",
    "## Voting\n",
    "1. hard: output class with highest number of votes\n",
    "2. soft: output class with highest classification probability"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57aef7b2",
   "metadata": {},
   "source": [
    "### hard voting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6b25b056",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_ex, y_ex, shuffle=True, test_size=0.2)\n",
    "logistic_clf = LogisticRegression()\n",
    "random_clf = RandomForestClassifier()\n",
    "svm_clf = SVC()\n",
    "\n",
    "voting_hard_clf = VotingClassifier(\n",
    "                estimators=[('lr',logistic_clf),('rf',random_clf),('sv',svm_clf)],\n",
    "                voting=\"hard\")\n",
    "voting_hard_clf.fit(X_train,y_train)\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d7cacce8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression 0.9666666666666667\n",
      "RandomForestClassifier 0.9666666666666667\n",
      "SVC 1.0\n",
      "VotingClassifier 0.9666666666666667\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "for clf in [logistic_clf, random_clf, svm_clf, voting_hard_clf]:\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "432af527",
   "metadata": {},
   "source": [
    "### soft voting\n",
    "1. for SVC Classifier, parameter probability should set to be <font color=blue><b>True</b></font>, then SVC will use cross-validation to calculate predict_proba variable.\n",
    "2. originally SVC would not calculate predict_proba during training process, since there's no clear Discrimination Function in SVC process, SVC will just solve one convex optimization problem and output one ideal supporting vector, which stands for norm vector of classification hyper-plane"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5e8c5cae",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_ex, y_ex, shuffle=True, test_size=0.2)\n",
    "logistic_clf = LogisticRegression()\n",
    "random_clf = RandomForestClassifier()\n",
    "# probability set to True, then SVC will use cross-validation to calculate predict_proba variable.\n",
    "svm_clf = SVC(probability=True)\n",
    "\n",
    "voting_hard_clf = VotingClassifier(\n",
    "                estimators=[('lr',logistic_clf),('rf',random_clf),('sv',svm_clf)],\n",
    "                voting=\"hard\")\n",
    "voting_hard_clf.fit(X_train,y_train)\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9ecd2996",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression 1.0\n",
      "RandomForestClassifier 1.0\n",
      "SVC 1.0\n",
      "VotingClassifier 1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "for clf in [logistic_clf, random_clf, svm_clf, voting_hard_clf]:\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17559f8c",
   "metadata": {},
   "source": [
    "## Bagging & Pasting\n",
    "1. bagging is short for \"bootstrap aggregating\"\n",
    "2. basic concept is training data with the same training algorithm for every predictor, but training each predictor on different random subsets of training set.\n",
    "3. when <font color=maroon><b>sampling is performed with replacement</b></font>, this mothod is called <font color=maroon><b>bagging</b></font>, <font color=maroon><b>when sampling is performed without replacement</b></font>, it is called <font color=maroon><b>pasting</b></font>.\n",
    "4. the ensemble will finally aggregate all predcition results of each single predictor, and find the most frequent appeared result, set it and the ensemble result. As in a statistic mode, <font color=maroon><b>central-limit theorem</b></font>, this aggregation would considerably reduce variance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d55d22d1",
   "metadata": {},
   "source": [
    "### sklearn interpretation\n",
    "```doc\n",
    "BaggingClassifier(\n",
    "    base_estimator=None,\n",
    "    n_estimators=10,\n",
    "    *,\n",
    "    max_samples=1.0,\n",
    "    max_features=1.0,\n",
    "    bootstrap=True,\n",
    "    bootstrap_features=False,\n",
    "    oob_score=False,\n",
    "    warm_start=False,\n",
    "    n_jobs=None,\n",
    "    random_state=None,\n",
    "    verbose=0,\n",
    ")\n",
    "\n",
    "base_estimator : object, default=None\n",
    "    The base estimator to fit on random subsets of the dataset.\n",
    "    If None, then the base estimator is a DecisionTreeClassifier.\n",
    "\n",
    "n_estimators : int, default=10\n",
    "    The number of base estimators in the ensemble.\n",
    "\n",
    "max_samples : int or float, default=1.0\n",
    "    The number of samples to draw from X to train each base estimator (with\n",
    "    replacement by default, see `bootstrap` for more details).\n",
    "\n",
    "    - If int, then draw `max_samples` samples.\n",
    "    - If float, then draw `max_samples * X.shape[0]` samples.\n",
    "\n",
    "max_features : int or float, default=1.0\n",
    "    The number of features to draw from X to train each base estimator (\n",
    "    without replacement by default, see `bootstrap_features` for more\n",
    "    details).\n",
    "\n",
    "    - If int, then draw `max_features` features.\n",
    "    - If float, then draw `max_features * X.shape[1]` features.\n",
    "\n",
    "bootstrap : bool, default=True\n",
    "    Whether samples are drawn with replacement. If False, sampling\n",
    "    without replacement is performed.\n",
    "\n",
    "bootstrap_features : bool, default=False\n",
    "    Whether features are drawn with replacement.\n",
    "\n",
    "oob_score : bool, default=False\n",
    "    Whether to use out-of-bag samples to estimate\n",
    "    the generalization error. Only available if bootstrap=True.\n",
    "\n",
    "warm_start : bool, default=False\n",
    "    When set to True, reuse the solution of the previous call to fit\n",
    "    and add more estimators to the ensemble, otherwise, just fit\n",
    "    a whole new ensemble. \n",
    "\n",
    "n_jobs : int, default=None\n",
    "    The number of jobs to run in parallel for both `fit` and\n",
    "    `predict`. ``None`` means 1 unless in a\n",
    "    joblib.parallel_backend context. ``-1`` means using all\n",
    "    processors. \n",
    "\n",
    "random_state : int, RandomState instance or None, default=None\n",
    "    Controls the random resampling of the original dataset\n",
    "    (sample wise and feature wise).\n",
    "    If the base estimator accepts a `random_state` attribute, a different\n",
    "    seed is generated for each instance in the ensemble.\n",
    "    Pass an int for reproducible output across multiple function calls.\n",
    "\n",
    "verbose : int, default=0\n",
    "    Controls the verbosity when fitting and predicting.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0f7522ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BaggingClassifier : 1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "bag_clf = BaggingClassifier(\n",
    "            DecisionTreeClassifier(max_depth=8, min_samples_leaf=10),n_estimators=500,\n",
    "            max_samples=100, bootstrap=True, n_jobs=1\n",
    "            )\n",
    "bag_clf.fit(X_train, y_train)\n",
    "y_pred = bag_clf.predict(X_test)\n",
    "bag_score = accuracy_score(y_test, y_pred)\n",
    "print(bag_clf.__class__.__name__, \":\", bag_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "74126b0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from matplotlib import pyplot as plt \n",
    "import matplotlib as mpl\n",
    "import numpy as np\n",
    "\n",
    "X_moon_bagging, y_moon_bagging = make_moons(n_samples=500, shuffle=True, noise=.12, random_state=42)\n",
    "X_moon_trian, X_moon_test, y_moon_train, y_moon_test = train_test_split(X_moon_bagging, y_moon_bagging, \n",
    "                                                                        shuffle=True, test_size=0.2,\n",
    "                                                                        random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "02e1430c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=8, min_samples_split=10)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_dt_clf = DecisionTreeClassifier(max_depth=8, min_samples_split=10)\n",
    "base_dt_clf.fit(X_moon_trian,y_moon_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "62b13e9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingClassifier(base_estimator=DecisionTreeClassifier(max_depth=8,\n",
       "                                                        min_samples_split=10),\n",
       "                  n_estimators=500, n_jobs=8, random_state=42)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bagging_dt_clf = BaggingClassifier(\n",
    "                base_dt_clf, n_estimators=500,\n",
    "                bootstrap=True, n_jobs=8,\n",
    "                random_state=42\n",
    "    )\n",
    "bagging_dt_clf.fit(X_moon_trian, y_moon_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b0c052a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_moon_class = [[],[]]\n",
    "for idx,(data, label) in enumerate(zip(X_moon_bagging, y_moon_bagging)):\n",
    "    if label == 0:\n",
    "        y_moon_class[0].append(data)\n",
    "    else:\n",
    "        y_moon_class[1].append(data)\n",
    "y_moon_class = np.array(y_moon_class,dtype=np.float64)\n",
    "\n",
    "X_moon_range = np.linspace(X_moon_bagging.min(axis=0)[0]-0.05, X_moon_bagging.max(axis=0)[0]+0.05,500)\n",
    "Y_moon_range = np.linspace(X_moon_bagging.min(axis=0)[1]-0.05, X_moon_bagging.max(axis=0)[1]+0.05,500)\n",
    "X_moon_mesh, Y_moon_mesh = np.meshgrid(X_moon_range, Y_moon_range)\n",
    "Z_moon_mesh = np.zeros_like(X_moon_mesh)\n",
    "for idx, xdata in enumerate(X_moon_range):\n",
    "    for idy, ydata in enumerate(Y_moon_range):\n",
    "        Z_moon_mesh[idx,idy] = base_dt_clf.predict([[xdata, ydata]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "df0dc609",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/collinsliu/opt/anaconda3/lib/python3.9/site-packages/joblib/externals/loky/process_executor.py:702: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "Z_moon_bag_mesh = np.zeros_like(X_moon_mesh)\n",
    "for idx, xdata in enumerate(X_moon_range):\n",
    "    for idy, ydata in enumerate(Y_moon_range):\n",
    "        Z_moon_bag_mesh[idx,idy] = bagging_dt_clf.predict([[xdata, ydata]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "01d86d73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fbfb55079a0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zgtaNmGMvEPhISkoK27ZtY+vWrfZlOp2O+vp6p1J33lCpVG7XHTt2jIceeoi///6bLVu2sH///iZf+N7o27cvRUVF9tI9AwcOZM+ePbz66qt+HceRd999F4PBYO+88HaOgQMHYjKZ+PXXX+3HWL9+PRMmTODgwYM+nfPHH3/khRdeYOLEidxzzz2sXbuW7t27u0y044oTTjiBhIQEfvjhB/synU7Hhg0bnEboPX0eLUHfvn0xm82oVCr7vZRlmUWLFlFeXk5OTg6xsbHU1NTY1+fl5bF48WL+/PPPsLZdIBAIWiPHjh2jtLTUPjL6+uuvM336dJ5//nkuueQSEhMT7S+5AL///jufffYZRUVF/PHHH+Tn5zcZYZ4zZw5vv/02O3bsoKCggBdeeMHvdnnylaIo3Hbbbfzwww9s27aNoqIiFixY4Pc5HKmrq+ODDz5g6NChdOrUyScfDRw4kLVr1zodZ/bs2Tz66KM+n/ff//43Op2OGTNm8Morr/DCCy+wevXqJgkF3TFkyBC2bt1KWVmZfdkPP/yAWq126lyIBP8fPnzYfi8HDhzIZ599xpdffmlfH+znOUHrRrzYCwQ+MmzYME488URuuOEGVq5cyerVq/nnP/9JWlqayxDrQEhISECSJJ555hmOHDnC33//7de8NIDrrrsOg8Fgn+v+4YcfcssttziNHPjCgQMHWLduHQ8//DB33nkn5513HmeeeaZP5zj99NMZNmwYM2fOZOXKlXz55ZfccccdjBo1yudcARqNhvvuu4/Fixfz66+/8vbbb7N//36fe9i1Wi233XYbzz77LM8++yyrV6/mkksuwWg0OiUbCjennnoqY8aM4aqrruKTTz7hm2++4brrrmP79u107NiR+Ph4brnlFh5++GFeffVV1q5dy3XXXcenn37aYtEGAoFA0NrZuXMnv/32G59++ikXX3wxiqJw4YUXApCRkcHGjRv5/vvv+eCDD+yJ2WzVTbRaLeXl5bzyyiuUlJSwe/dujh496nR8rVbL66+/zp49ezhw4AB79+51qo7SXCRJIi4ujhdffJGioiIKCgooLCx0GqH2BbPZzG+//caqVauYNGkSx44d48knnwTwyUd33HEHP/74I3PnzuXHH3/kkUce4eeff3bKBeCNLVu2cOutt/L111/z008/8cEHH6DVamnfvr1P+8+cOZO4uDguuugivvnmG1566SUeeughZs2aZQ+5jwTmzZvHp59+ysKFC1m7di1PPPEEjzzyiH0KR7Ce5wRtBxHLIRD4iFqt5qOPPmLevHncfPPNmM1mhg0bxhdffOFTaLkvJCQkMGXKFF599VVeeOEF+/y9/v378+mnn/oktczMTD777DPmz5/P9OnTSUtL44orruCee+7xqy3PP/88L7/8Mjk5Ofz73//mxhtv9Pkcsizz8ccfM2/ePGbNmkVMTAwTJ07k4Ycf9llIo0aN4oUXXuDZZ59l4cKFJCUlcfnll9sT4fnCvHnziI+P58UXX+TYsWOMGDGCr776ii5duvh1L0LNu+++y5133sl1112HJEmcccYZPProo/ZQwgULFiDLMo8++ijl5eWcdNJJrFq1im7duoW55QKBQNA6mDFjBmCNzuvXrx///e9/GT9+PABPPfUUs2fP5sILL6Rz585cccUVrFixgnXr1nHllVfSs2dPBg8ezJNPPklNTQ319fUAnHLKKXz00UfEx8dz4YUX2l/gKisrURSFzMxMPvjgA0aNGhWUa7jwwgt56623WLFiBTU1NSiKQteuXVm1ahW9e/f26Rg1NTWccsoppKenM2bMGJ577jl7yTvw7qOzzjqL999/n/vuu4/XX3+dnJwc3n77bads8N54+eWXWbBgATNnzqSmpoa+ffvy4Ycfupyn7or27dvz9ddfc9ttt3HxxReTmprKrbfeyh133OFzG1qC0047jTfffJNHHnmE5557ju7du7NkyRL7YFGwnucEbQdJp9P515UnEAhCxptvvskTTzzB008/TVpaGhaLhT///JPbbruNd999l3POOSfcTRQIBAKBQODA/fffz+rVq+2d0EajkbVr17Jw4UJ+/fVX4uPjGTZsGEuWLKFnz55IkmSvfjJlyhSnWveB8v333/PPf/6TJUuW0LFjRxRFIT8/n7lz57JgwQKuv/76IFypQCCIZMSLvUAQQRQXFzNv3jx+/vlnSktLUalU9OjRg2nTpnHHHXeIhCkCgUAgEEQYu3fvZsGCBWzatImKigpiY2Pp3bs3l156KbNmzcJkMnHvvffy6aefUlxcjMlkomPHjpxxxhncc889PoeYe6K6upr58+ezevVq+zSAzp07c9ZZZ3HPPfc0ybwuEAhaH+LFXiAQCAQCgUAgEAgEgihGZF8QCAQCgUAgEAgEAoEgihEv9gKBQCAQCAQCgUAgEEQx4sVeIBAIBAKBQCAQCASCKEa82AsEAoFAIBAIBAKBQBDFiBTbPmKxWCguLiYxMdFeW1ogEAgEgnChKAo1NTVkZWUhy6KfPhgI1wsEAoEg0vDV9+LF3keKi4vp3bt3uJshEAgEAoETu3fvplOnTuFuRqtAuF4gEAgEkYo334sXex9JTEwEYPTtnVDHipERgUAgEIQXk97CuicO2v0kaD7C9QKBQCCINHz1vXix9xFbSJ46VkYdJ2Qf6SgKiChKgUDQFhAh48FDuD66EK4XCARtCW++F9YStDoUi8zGFz9FsYhfb4FAIBAIWiPC9QKBQOCM+DYUtDoqCoajO9KHisJh4W6KQCAQCASCECBcLxAIBM6IUHxBq8CgS8NUnwRA0boZKJYYin6eQWxyMQDquGpitOXhbKJAIBAIBIJmIFwvEAgE7hEv9oJWQeGa2Rz49XLU8RUoZg0AFQUj+G3pCkx1qXQe+Ra5kx4IcysFguhDJalJi2+HJIkAr5ZGUSyU1x3DrJjC3RSBICIQrhcIQkesKo6k2DTh+xZGUSxU68vRm+ubfSzxYi9oFeROegBth53kfzsXoy4VAFN9KjFJh+kzdR7ZQ1aEt4ECQRSSEpvOFUPmkRyXJhJUhQFFgar6ct747VGq9GXhbo5AEHaE6wWC4CMhcVrP/2N41zNQyxrh+xZGUcBkMbJh/2q+z/8IBSXgY4kXe0GrIXvIcsrzR3NkyzT7srSe64ToBYIAkJCYmHsJWWldSExPQHi+5VEAbVkiZ+Zewn//fLFZshcIWgvC9QJBcDmt5/9xSs5k0jLSkDWS8H0LowAWo8IpmskAfJcf+HdZRMdalJaW0rdvXwoLC33a/rXXXqNnz56kpKQwZcoUiouL7eumT5+OVqu1/zvnnHNC1WxBmFAUqNg3Ak1CKcmdf0eTUErFvpEo4llYIPCbhJgkctsNQJsSj0ojIYt/Lf5PpZHQpsST224ACZqkcP9KhAzheoE/CNcLBMEjVhXP8K5nkJaRhiZBFr4Pk+s1CTJpGWkM73oGsaq4gD/PiH2xLykpYfr06T6Lft26ddx///385z//4a+//kKv17NgwQL7+t9//50NGzZw8OBBDh48yIcffhiqpgvChO5IHmaDlr4XzmHoddPoe8EczAYtuiN54W6aQBB1xKu1qGQVkkr03YcTSSUhSyriNdpwNyUkCNcL/EW4XiAIHkmxqahlDbJGuD7cyBoJtawhKTYt8GMEsT1B5YorrmD69Ok+b79nzx4WLVrE6aefTqdOnfjHP/7B77//DsDBgwdRFIV+/fqRmppKamoqWm3rfEhqyyRk5jN67slk9FoLQEbuWkbPHUNCZn6YWxbZiFEOgStsyXOE6sOLBEgSrTaZkXC9wF+E6wNH+F7QGEmSrY4Jd0MEQfF9xD4pLF68mBtuuMHn7a+44gqmTp1q/3nPnj307NkTgE2bNmE2m8nNzaVdu3ZcccUVlJeLciitDVltQB1X47RMHVeNrDaEqUWRj2KR2fjipyiWiP0qEAgErRjheoG/CNcHhvC9QND6idi/7h49egS8b2lpKa+99hozZ84ErOIfNGgQq1at4qeffqKwsJCFCxd6PIZer6eqqsr+r7q6OuD2CASRSkXBcHRH+lBROCzcTREIWoQDRQfJyx4Y7mbw5WffcPqwsxg7aDyfffxFuJsTNoTrBYKWQfhe0JZoq66P2Bf75nDLLbcwYsQIzjrrLADmzp3LypUr6du3L3l5eTzwwAOsXLnS4zGefPJJsrKy7P969+7dAi0XCIKHu5A7gy6N2tKu1JZ2pWjdDBRLDEU/z7AvM+gCn9sjELhDhIA2sHvnHm6fPZ9ZN8/kP+8t4fknXyT/74JwNyvqEK4XCKwI3wsiBeH6BsLh+lb3Yv/mm2+ydu1alixZ4nablJQUSkpK0Ov1breZO3cuxcXF9n+7d+8ORXMFgpDgKeSucM1s1i9azW9LV1BRMByAioIR/LZ0BesXraZwzeyWbq6glWM2w/ln9cJsDndLIoP/vvcxI0YP44LL/o8+J+Ry2VUX88mKz8LdrKhCuF4gsCJ8L4gUhOudCYfrW9WL/aZNm7jzzjt588036dChg335ZZddxoYNG+w/b968mQ4dOhAbG+v2WLGxsSQnJ9v/JSW13lJDgtaHp5C73EkP0GfqAiTZgqk+FQBTfSqSbKb31HnkTnqghVsraO1sWq9l9844ftvQMonM/tq2g4um/JPBvUZyyblXsGfX31732bR+M+eNv5CTeo5g+tmXsnvnHvu6tT+sY9Ip53FSzxFccu4V7C8osq/7ZMVnnD78bAbljOCay66nvKzC67l2/rWLkScPt/884KT+/LVth38X2YYRrhcIGhC+F0QKwvXOhMP1UfdiX1VVhdFobLL8yJEjTJ8+nVtvvZVBgwZRU1NDTY01uUr//v2588472bhxI1988QX333+/fU6eQNBa8CfkLnvIctJ6rnPaP63nLxz89UqRWEcQFMpLVewviGF/QQxvLM3EZJR5c2mmfVl5qSok562pruGay67n9Inj+OKnVQwcMoDbb1jgcR+LxcKca27jrCkT+ObX/zFo6ECeeOAZ+/p5c+7m/Eum8eXaT8jNy+HZxxYDoNPVsuCWe7ltwU18tuZjVCoVr730JgAj+53KsLwxTf7t+HMnNdU6OnftZD9+YpKWI4ePhuBuRC/C9QKBe4TvBZGCcH1kuV4d0qOHgBEjRvD4448zZcoUp+XLly/n2LFj3Hfffdx333325Tqdjrlz57J//36mTJlCu3btuOaaa5g7d25LN10gCCmFa2Zz4NfLUcdXoJg1QEPInakulc4j37L3zisKVOwbgSahlPj0/dSVdaVszykY65KoKBxGWo/1Hs+lKNaSHAKBO5Ysas97b2SSnGrCZLT+smz8VcvF5+ZQVaHm0itLWHB/cdDP+/03P5KSmsK1N/4LgFlzZnLiSf297rfy2+UkJSWya8dudDU6CvIb6qrHxsViNBhITE7kvsf+jcViAUAly6jVagwGIxkZ6Sx58zmU4xMMV3z1vv3/HWnfoT1qtYqYmJiG48fGUl9X36zrbm0I1wsE7mkp3wvXC7whXB9Zro/4rjqdTke3bt3sP+/YsaOJ6AFmz56NTqdr8g9Ao9GwZMkSDh8+zLZt25g/fz5qddT1aQgEHvEUctenUcid7kgeJr2W3lPuou8Fc8g58yFM9cmgeE+sI0rmCHxhwf3FLHz8ACoVVFVav2+rKtWoVXD/EwdCInqAI8VH6NQl2/5zSmoyk6ae5XEfWZZ54+W3OXXIBO5f8AjV1TWYzRb7+qdefIwN6zZxyklncOnUK+2hdHHxcTy79Ak+fGcFoweM4+pLZ1FUeACATp2z6dylU5N/MTEaUlJTKCstsx9fV6NDo2nbThKuFwh8xx/f1xzOw2zw3/fC9QJfEK6PLNeLv1aBoBXhOuRuHdlDVjgti08vID7tIJX7h7N+0Wr2fj0fWWN9OPaWWEeUzBH4yvSLyxl5snO96RFjavi/i0JXW7xjdkcO7D9o/1mnq2XKaf/HsaMlbvdZv24j77/1If9bs5IVX77P9Eum2dfV1tZSV1vHa8te5te/fmLoiEEsuPVeAMrLKkhOSeb9T95k7dbvyMhM55F7n/DaxgED+7Fl8zb7zzu276J9x/aBXK5AIGij+OJ7xSKz46OnGHXrWKqKhvnle+F6ga8I17smHK4XL/YCQTMIZVmPQI7tGHKX3Pl3NAmlVOwb2eRYVQdOQne0F+36fmXv9TfrUwHXvf6iZI4gEBQFNvyiJS3dxImDdKSlm9i4ThvSv5tx48dSVVnFS8/9h8OHjvDSs0sxmy1ktstwu0+trhaA6qpqNm/4nUcXPmn/A7RYFK69fDafrPiM8rJyJFlGOR6eV15WwVUXzeSn73+mplpnXefDxU08Zzz/W/Ulf+/eS21tLe+89j5jxo0OwtULBIJQEGmut+3nzffWl/Ncao6c4DTK7873wvWCQBCud004XC9e7AWCAPEnTM3fL7dAQ+B0R6whd30vnMPQ66bR94I5mA1adEfy3Ao7tft6kjptcTpO415/UTJHEAi7d8RRVyvz5AuFfPi/vTyxuBCdTsXuHXEhO2diUiKvvPsi33+zhkmnnscfm7ex+NVnkDxMFB172smcPnEc5595MffOe5ALLvs/jh45RsmxUhITtTz+/MO8tOg/TBw9me+/WcO9j94NQM9e3bnznttYOO9BJow6h4K9Bcy9+2avbczr14fLrrqYaRMvYtyQiaAoXHrFRcG6BQKBIIhEouvBve8rCoa7fTlP7b6elK6bnI7j6HvhekEgCNe7Jhyul3Q6XQj7U1oPVVVVZGVlccrdXVDHif4QAZTnj+SPN97ipKv+6Tn5jEVm45JVDJs1FUm2uN0ukGM3xmKKwWKKQR3XEBJlqk9CVuvZ+/WdTsl2TPWpqOMqkFRGjLpMJHU9SR13UlfWFVmtZ/Tto52S5hz67QLyv52LobqhvFRM0mF6jn+iSai/IPppp+3EdSMX0iG7PbImsOxJBr2EwSCRmNTwe19dJRMbqxATK9Sz66/dHDl8lJEnDycmNsblNhajwpFDR3np14Uc0x10Wmeqt/Djg0UUFxeTnJzcEk1u9QjXCxoTia4H977P//ZWDm74h0vXm+pSkVR6VBq9PZFeY98L17cthOtDjy+uh+D4XlhLIPCDQMLUfJ2nFowQOFltcJI8gDquGlltcJtsB0VCUtdx4j+uaTLK74iv8/cFAhsxsYqT6AGSki1C9Mfp07c3p5w+xqPoBQJByxPprgf3vu89+T63ifW6j1uErDK7jOqzIVwv8Bfhes+0pOtFuliBwA98LTFj0KVhqk8CcBJ3bLI1O6g6rpoYbXlAx24O2UOWU7Z3NEe3NiQMSeu1lrxz77Y/IGTkrmX03DHIar3Tvq5K5tjm84lyOAKBQCBoLbQG15fnj+bIFgfX91xHt1OW0mX0Gx59L1wvEEQv4sVeIPCD3EkPoO2wk/xv52LUpQLWnvCYpMP0mTrPaZ6av+L29djNkauiQPnfYwGFhHZ/Y9SlU1kwHFVs017/xtjm8/W7+AYyeq2ldM8Yti97Ad2RPBI77gysQQKBQCAQRBitwfUV+0agiq3ErE9CFVtNxb6RSCoDarXBadvGvheuFwiiFxGKLxD4iS9hav7UmIWGhDvejm1LtGMx+/enawv9K919Gqb6FEAiISP/eD3bJHuSHE8kZOYzeu7JZPRaCzT09Cdk5vvVFoFAIBAIIp1odD1YfW91fRLa9n8DMtr2ezDVJ1G6+zSvof7C9QJB9CJe7AUCP/G1pJyv89Qcs+J6O3ZFwXBqDvdh/XPf+pVF15bpdseKJ5A1tcePNYK9X8/HYorj2PazvR7D0/x9gUAgEAhaE9HoerD6ftu7S5HVBmqP5QBQe6wXstrAtneXes1sL1wvEEQv4sVeIPATTyXlHPH1ocAx4Y6rY5v0iZTsGmdPtIMSQ11pDw5vmeJzoh37qILKjFmfAjiPKvSefF/Q7o9AIBAIBNFONLoeHH1vco4iUBldRhEIBILWg5hjLxD4iS1MzVuyOU/z1GKSjrhMuJMz8XEGzbiY2JTD9mN3HLiSbe+8gjq+yj6HDyR2fPwEKBJZgz4m58xHmiToaYy7ZDoi061AIBAIBM5Eq+tB+F4gaKuIF3uBwE9ktaFJSJqrZHOeHgoa15QHa2j85lc/aJJwp8+591BztBeVBaMAh0w6FjWypo7Df/wfyAZOOO9uj+1ubZluo7XdAoFAIIh8otX1IHwvELRVRCi+QBAiAqkp7y7hTnL2DqBxPVAZiykOxQIlf53ldR6er2GF0YDjXEWBwB8OFB0kL3tguJsBwO+btnDWmHPD3QyBQNAMIs31IHwvELRV14u/EkGbpfH8t5bG14Q7AL3OfgBVXCWNhS+pDHQdsxRTfTIVhcM8nq81Zbp1nKsoiBLC/QcXYWzfuoMb/3ULBoMx3E0RCFo14f7qaWnXg/C9IIyE+w8uwmhp14sXe0GbJBJ6gH1NuAPW3ncUGUnVKCutIqM71ss+b6+2tKvbJDvRnunWVrLPlljIl2sWRAiKmW6bLgbFHO6WRAS1tbXc+K9buOSKC8PdFIGgVdMWXQ/C94IwIVzvRDhcL17sBW2SSOgB9idULiEzn4H/vArFrEGOqSQm8SigoJhjKM8fCVjn7f22dAXrF632Ws4mGrGV7Ptt6QoqCoYDrf+aWwvxFZuJ1f1NfMXvLXK+v7bt4KIp/2Rwr5Fccu4V7Nn1t9d9Nq3fzHnjL+SkniOYfval7N65x75u7Q/rmHTKeZzUcwSXnHsF+wuK7Os+WfEZpw8/m0E5I7jmsuspL6vwei61WsP7n7zJ0JFDAro+gUDgG8L10YnwfXQiXO9MOFwvXuwFbYZI6AG29dArin+hcrLagKwxooqtZsClNzBm3nA6j34FULAYkwHP8/ZaA/7OVRSEF5WhHE1dEZq6ItKK3kFSTKQdeMe+TGXwntk5EGqqa7jmsus5feI4vvhpFQOHDOD2GxZ43MdisTDnmts4a8oEvvn1fwwaOpAnHnjGvn7enLs5/5JpfLn2E3Lzcnj2scUA6HS1LLjlXm5bcBOfrfkYlUrFay+9CcDIfqcyLG9Mk387/txJTIyGDlkdQnL9AkFbR7g++hG+jx6E6yPL9SIrviBkRFoW08I1s11mp/1t6Yom2WlDgWKR2bhkFUOvncamlz9m2KypqOPcZ9xtfP+07Xdx8u0NmXd7T3oYfVUWx/6cYt+mueVsIu0za4wo4RM9pBe+QtrBZZjVyaBY55bFV2yi62+XozJVUd7pIo7l3hH0837/zY+kpKZw7Y3/AmDWnJmceFJ/r/ut/HY5SUmJ7NqxG12NjoL8Qvu62LhYjAYDicmJ3PfYv7FYLACoZBm1Wo3BYCQjI50lbz6HcvyJfsVX79v/35H2HdoH4zIFgogiktwR7a5P7LCL0XNPRhMfOte7Om+kIXwfHQjXR5brxYi9ICREwry2xoS7B9gWEnhg/T+9hgY2vn+KRea3pStQxdQ2bKNA1f4hPs3b84VI/Mwa489cRUF4OZZ7B4d7340iqVCbrA+xalM1iqTicO97QiJ6gCPFR+jUJdv+c0pqMpOmnuVxH1mWeePltzl1yATuX/AI1dU1mM0W+/qnXnyMDes2ccpJZ3Dp1Cv5a9sOAOLi43h26RN8+M4KRg8Yx9WXzqKo8AAAnTpn07lLpyb/YmI0LtsgEEQrkeaOaHY9gCSb+P219xv8H2TXuztvpCF8Hx0I10eW6yP3L1oQ1UTCvDZX+JOdNhjYQgIrDwxg3w+zUSwx7P/pOhRLDAXf30DlgQEuQwMb3z9X9zPY5Wwi9TNzpDWV8GkLVGVPoy7N+fepNm04VdlTQ3bOjtkdObD/oP1nna6WKaf9H8eOlrjdZ/26jbz/1of8b81KVnz5PtMvaRghqq2tpa62jteWvcyvf/3E0BGDWHDrvQCUl1WQnJLM+5+8ydqt35GRmc4j9z4RsmsTCCKRSHRHtLre1bJQeC8SP7PGCN9HD8L1kYMIxRcEDYMuDVN9EoDTvLbY5GLAGnoWow3NXBtfcewBjk/fT11ZV3sPcChC0mwhgZLKhMUYB4Chph0A5flj2fyfoWDR0HnkW3Q7dXGT+1fww2wUi0Thmhua3s/4MkbPbQjNt83bk9V6n9sXDZ+ZI7a5is25ZkELoijEV/yGSZOKMa4LmvoiEio2hTQGdNz4sTxy7+O89Nx/OG/6FN59/X3MZguZ7TLc7lOrs0bCVFdVs3/ffh5d+KR9kqzFonDt5bN58Ml7GXHycCRZRjkenldeVsFVF81k8avP0Kdvb+s6MZwkaANEujuizfWFP16HYrE2bP9P1zrfT8nI0OvPISHdOkIYqPci/TNrjPB9FCFcHzGIF3tB0AjnvDZfvztsPcD9Lr6BjF5rKd0zhu3LXkB3JI/EjjuD3q5eZz3E0T8noigxGIzxx5faAmUkNPFV9Bz/BNlDVrDn8383uX/le8dQvne0fR9v99Nx3p4NT/cmFJ9ZKOftyWpDk3I9rq5ZEBnE6vYgm2s52PcJ6jJGkVC6juy/7iBWtwd9Yu+QnDMxKZFX3n2R+xY8zNLnX6XfiX1Z/OozSB5+KceedjKnTxzH+WdeTKeunbjgsv/j6Yefo+RYKZntMnj8+Yd57vEX+Pft99O1exfuffRuAHr26s6d99zGwnkPUnKslLy+vXnwqYUhuS6BIJKIdN9HnevzR1O2Z5x1a5X1xdXT/fTX9RD8zyzUc/SF76MH4frIQdLpdJHZ5RBhVFVVkZWVxSl3d0EdJ2YwuOPQbxeQ/+1cDNUNWSBjkg7bhRYKbIlqhs2aiiRbPG5rMcVgMcU41Xc11Schq/U+13f1R2bl+SP54423SO2xnvK9Y5us7zDwI/pdcKv9Z1f3D8kMisr+oz/305d7E8zPzJ/PQhDZtNN24rqRC+mQ3R5ZE9jTm2QxIFkMWNSJ9mWyqRpFjkWRY4LV1FaNxahw5NBRXvp1Icd0B53Wmeot/PhgEcXFxSQnJ4epha0L4XrfiWTfR6PrVXGVoIBZn2Jf5uv99PW+BOszE65vPQjXRw7B8L2wliCoBGtemz8RLv7MFZPVBifRg7UH2GfR+5BwxlWpncrCoajjy5FkA2BBkg2o45smgnF1/2JTnP+4/bmfvtybrMHBm4sYDfP2BC2HIsc4iR7Aok4SohcIWgHB8L2/0ay+OiYaXZ/ZZzWZed85LfP1fvpyXxQleM9owvUCR4TrIwcRii8IKsGY1+ZLT3C45oo5yiytx3qX27gKd7OYYkEyolg0JHfZQO2xPHqOf5L8b+Y5hQa6un/6yk6o40tJyPDtfvpzbxSLzIYXV2GqzfTrHP6eT5NQHtFldQQCgUDgH831va+jvuHwfThcb7t3vt5Pf12/cckqhl43lfL8wD4z4XqBIPKJ6Bf70tJSxo4dyxdffEG3bt28bv/TTz8xZ84cSkpKmDt3LjfddJNP6wTBIxjz2gIVaqBzxbwJTV+Thlnv+0NF7qQH0HbYSf63czHqUo8vlVHF1pJz5qN0GfmePSSw48DP7IlgFKXp/Tuw/hJ2f/oQPSc8Sefh73u9n4ri/t5senkF5nrne2O913nIaj2xSccYMvN8yvaO9usz8/ZZdBrxNhUFw5we3iK9fq5AIGg5hOujk+b63hfXQ8v53t8OhGC6vnTPGP58/yVAof+ls7zeT0+u/23pCoy1qXQZ1dj1fSj+fTqGmg6c+M8ryez9k1+fmXC9QBD5RGwofklJCdOnT6ewsNCn7Y8dO8aFF17IBRdcwHfffceyZctYs2aN13WC4GLLYprRay3QkMU0ITPf436uQtqKfp5hX9a4REyw6tR6C7dTLDIbnvuG9YtW89vSFVQUDAcaZLZ+0WoK18xusp+r8Pb0nHV0Gfke0BASaPuvrR3x6QVO9y97yApGzx1N9mBrmJyn+2k7Rq+zHnJ5b8z1qfQ+dz7dTl3sdK9RNGjb76K+ojNHtp1DUvZ2nz4zG94+i3YnfO0UshcN9XMFAkHLIFwfvQTie39dDy3n+4IfbuTXZ9bw29KPfHZ99pDlpPZovuszctcy6tZTGHXrKV7vpzfXI5mJTT5C17EvNrnXxZsuQJIsmOpSMejSfH5GA+F6gSAaiNi/tiuuuILp06f7vP2yZcvo2LEj8+bNo1evXsyfP58333zT6zpBcAl0Xlvhmtl+vzwHY66YrRe7vMD1PLHyfcMx1SfTefR/fH6osMnMFu6W3Pl3NAlN59i5akfVwYH2+6co1vsZl3rY6f65u5+Oox+u7o2iqEjIKLLf600vf0R5/kgAaooHoFg0/LV8EXu/mm8dudBrfbqH4PqzSOq0hdTuG5s8vB3eOhndkTwxNy/CURQLigIiu2p4UbB+FyhK60xQJVwfvQTi+0BcD8Hzfc1h1/PCFQXanfA1YEaxyD53IFjMMsf+PAd1fOCut50/JrGMmETnKQWu7qc31yd22I2huj27P7u/ieurDw10cn1taVcsZrXPuQeE61sfwvWRg833FsUc8DEiNhR/8eLF9OjRgzvuuMOn7bdt28app55qL3MwZMgQ7r33Xq/r3KHX69HrG2plVleLEhuhxFVIm6k+lZikw/SZOs+lvAOd3+cq3G7bu0sZep01fEyxqJBkq9y3vbcUxRJDXUlPkjptoXTnRPtx3D1UOIa397/0OrchdR7D/hSZbe+/yPAbJgc09zAmqZiyPWNQxVYQn3YQ3dFeKJZYin6eQa+zH0KjPca+729CMSVY76XFmuBE1tRTsus0Dv8xrUl4o6f72vizqD6cR+nOiVQVDXYo5zOKTS99jKkuDZAjun6uAKr05dQaqjHUpBOTqEZEU7Y8CmCoMVFrqKZK3zr/PoTr2xaBuB6C4/v9P88AJYb9axvco1hUgMK2918mPq0QUCOr652O4akD4fDv01EsGrqduoRuY/7jv+sBVYyOLW+9EVBeoZikYsr3jkYVV0586iHqK7KpKByGYolBMavpccZTwvUCj5TXHaOqvhxtWSLalHgklSR838IogGJW0FXWUVVfTkVdScDHitgX+x49evi1fXV1NXl5efafk5OTOXTokNd17njyySd5+OGH/WqDoHlkD1lOef5ojmyZZl/mSaiBzu9znCdmMVlFZNYns+nllVgMWrQddqE70gc5RmefW1++bwRmQxKgoNEeA0Xl9FDRVLoaUrv/SHzafmpLu9rD221z7Bq3o+n8uDRQpIDnHq5f9N3xEnkK+mojstqI2RBLef5o+3y4uPRC6kp6OR3PYkxAHVfV5AHLW5IjV5/F1neXgtIw8mE2JANmZE09FmNCi9U8FgSGwVzPf7ctYfqAWSRUJol5kmFAUaDWUM1/ty3BYK73vkMUIlzf9vDX9RAc35v01vryjh7UdthFzeE8UFToK7IBMFR3BKyek9T6Jh0Ijnl3jv01EZCoLBhG7QlfE5++n2HXTyY2+YjLNriam56Z93XAeYVsro9LK0B3tDeyphbFFGtfX1k0GMUU1+R4wvUCG2bFxBu/PcqZuZeQ224AsqQSvm9hbKP0e45t5as9H2BWTAEfK2Jf7P1FrVYTGxtr/zkuLo7a2lqv69wxd+5cbrzxRvvP1dXV9O7dO8itFjjib498QmY+o247GU28NZzNNlfM8eXZFbmTHiA2dT+Fa27ErE89vlTCYoyj6ymLSeuxnvL8Uexfey0c77e0bmeh49C3OfrHBfS7+Dp2/Pc5+0NFwQ83cuCXq9BoS1HM1j+ryv1DPMrM3ciFRnuMxA47qTnc32tPt7tjgELnUf8hseNu8r+di9lgLUNiNiShitXRe+o8ClbfhiahFEWRMNWl24/p6gHLW5Ij21xLW2hhRu5axs4bwc5VD3J021T7drLagMWYYG+nt1EaQXjZX7mHxb8sIDk2DUmK2JlbrRZFsVClL2+1L/WBIFwf/QQy+h6f0dQxvvi+26mL0WiPUbTuahST1YMWYxKyxkDn0a/Qvv8X7Fz5KLrD/Y533gPISJoyMCfSv5HrbXl3TPUpPifz8+T6HuOfoGr/0IAS9Tm6vvc5D9lr1Jv1Kfb1MUmHUcVVIssW4XqBW6r0Zfz3zxdJ0CQRr9EK37cwimKhzqij1liN0sxJEa3mxT4tLY2SkobQherqamJiYryuc0dsbKzTA4Ig9DTuCS7ZPYa/PnTfIy/JJn5/bYVTz7I6riGM0lOJGN3hfhh16U7LFUVm/9pZFP5wM5K6DlcpKOpKcrGY4qgr6+H0UNHuhK858MvlTebmeZOZq5ELU32SX5l/s4csp3T3OI5tP8fx7lBf3pXOI992OYUgpdN2zAYtfS+6gV0fP45iqcJsSEAdq7M/YBlrfc8QLKsNTeboqWKrqSwchiq2ErM+CVVsjb3Tw7EtQvSRjcFcT0ltcbibIRAAwvWtAX9dr1hkfltqdb0jNt976hDI//Y2Dm26CCTn0WdTbQpFa2dR9MuVqDVN55erNGZMRk0T11cUNOTdObLl/3yeTuDK9UZdOkU/X+Oz7z25vra0K6nd15PSdZPT+qSs7VTuH8oJF9woXC/wiIKCzliFzlgV7qYImkGrebEfPHgwy5cvt/+8detWsrOzva4TRA6OPcGKRSb/mzsZdespqGLqXG7vqWfZUzhZ4ZrZHP7DJlcF26g8lhgUi0JCu92Y6lMxVHdw2MuCpNFReywHkNj/4yziUg5iNiQQk1jCwfVX4O/cPGg6cqE7movZGI+p3ho65+vcw7I9p1qvRTaBRQXIlO+zlrgz1WYiq+tI7LjTPjKSd948Rs89mbqyruir23PiP64itdsflBcMZcd/n0V3JI/izRcEXGLIoEujsmggJn0S2va7qCoaSkLmbqoPDkYVW0V8RgH6ik5+1TwWCAQC4frop6VcD6DS6AEZLI0fd61T1WRZsXfGWzGAynh8frhr1/ubdwdcRykoigoki8+DAZ5cbytxp9Lonc5Rc7gvo247mfpy4XqBoC0QdbEWVVVVGI3GJsvPOeccfvnlF9asWYPJZGLRokWMHz/e6zpB5OCYYdcm8pojeU69w76WynF8EGhM7qQH6D7uueM/OVtG22EHI+ecSVLW9kZ7SSjGRIy6TGs7atrx5/svs2PFs2x56w17dt+GuXkWJHWdx+y40DBy0ffCOQy5dhr9L7kOSXLOhunL3ENJNtNl9FI0CWXY/qyt0wckZE0dA/5xDUOvm0bfC+ZgNmipK+2JOq4Ggy4dsLDnf/ehitHRLu97e+mb5pQYKlwzm23vvoKk0lN7zDqPv/ZY7+PtSiCt+0Z7W3RH8twexxFP9zHSiKa2CgSRiHB966WlXA9W3/c47VmX63qc/jTt+n7daKkGzPHHc9S4d335vhGU7joDUIhJPOo1G35j1/e9YA4oEknZfzpt5y2vkDvXS7KZ7qctQpLN9L1wjoPvE9BXdo4q10P0ODRa2iloO0Tdi/2IESP48ssvmyzPzMzkkUceYerUqeTk5PDXX3/Zs+x6WieIHHwRuadSOb8+u9pevsVbfdxup7xMu36fO51fUukx1aVhsUDN4RPQJJTZy9dIat3xUD7bn4wMkkJy1/XEJJY59PjLqOIqyJt2Oydedo1XmdlGLtJ7rmPji5+S1nMdGm2pz6VzHI/R66zHUMzOYafpOWsZc+dwp7q4w66fjCQbqS3tysH1l4MSQ11Zd45sm9yk9E2gJYZyJz1A3nkLkFVmp4Q6msRj5E27k9xJD/hVPzea6uFGU1sFgkhFuL710pKuB+g69mWkRtF0krqeLmOW2kfRk7K3OrjeIZLPjevN+lRQJLqMfpkx84Z7fXlt7Pr0nHWMum0MNcUn+FwW15Pr03quo/spSxk992S775OytzNoxsVIsjFqXA/R49BoaaegbRHxofg6nc7p5x07drjddubMmZxxxhns2rWLMWPGkJyc7NM6QWTgLXOsLRzMXamctJ7rOPzHNEp2neY1nExSGagoGAFYSMjMt2ahB0z6BEr+OhuzIaFJ9l05/iiG6ix7e2NTDjJ05sVsX/6M07y5zD4/2GXoLrmPLSTNNl+tPH8kNYf7cPiP87EYm57bU+Zf2zHK8kdiqktFFVuJtl0+dWVdqSwcjirWuc7wgV+v5MCvl6OKrUaxaGxHYcdHT4CiosPAj+h11iPEaMs9JjkCz2F1ruYUpuf87PSg4JgTwRPeEvpEEtHUVoEgUhCubzu0pOsBakt6ophiUcVWoW23F92xHMz6REp3neEy076vrm/X/zNyJz0KuE/m5871FYXD0MRXYjH6nunfk+sr9o1EUhlQO0Q92O5zc10vSZ7zGATT9RA9Do2WdgraFq2umyknJ4dJkya5lLmndYLw42s4mLue5UFXXeFzOJnuSB6KOYb+l85k5M3j6XvBHBSLmoGXzyC9149Ovd4ZuWsZeesYDDXtAQuyRgdY0Fd2wmxukKGrHnd1XHWTRDO2Xl59dYZ9hMFWX/fItgmcNOMi4tP2Y9Clee3pdhz5KPhuDiChbb+HvhfMIefMhzDpE63X6jACkDvpAVJ7/IKprqFkD4Bi1iCpazmyZRqFa2YDUFPcF311e06YfrNTKH9NcV+vPdWODwq+Rh+4uzZfRmXCSTS1VSBoDQjXRy8t6Xqwuk0VV03/S65n6HXT6H/x9ahia4hNPtws11ftH+rks8a+9+T6wp9mgGTkpBkX2af+efJ9uFyvO5LndWS6ua5vfH2R7NBoaaeg7RLxI/aCtoUv9W099Sz7Wh/XVckWW2974xdxgIr8MaBI9Jz4GN1PeZmCH64j/9s7OPjL1X7X1q0oGE7N4T7s/ux+SnZMRB1fgdlwvL7u3jP4441BmOsbRh089XS7GvmoPdbLPnKRPfR94tML2Pjip07JhQZddTkbX/qI6gODHY4mo46tpec599nvl21enup41mDbfaosGui1p7rmcGB1hz1dW6TWw42mtgoEAkG4aSnXAyR23MXJjXx/8u0nu/R9S7m+bM94Nh8Y7OR6cD+yHS7Xy2q915Hp5rre3fVFokOjpZ2Ctot4sRd4pSUzmfpS37ZxqRxHiWg77PSpPq6rki2eXqDTe/3I6NtHEZdyBIDu416i40mrkNV1ZA/9wGttXYOuoZyMrcfeVB9P51GvUrz5QizGxOM3QIVi0dDjjCfIHrocb3gKV7Rl1i3PH9lEyooChqqOSKp6FHOc/XipPdaRmfcdtaVdAezz8op+noGsqcWs16KK1XFw/eUeS+IoFpkdHz3FqFvHokmo8nhvmnNtkUI0tVUgEAhc0RpdD/75Xrje6nRjXQoAmvhKjyXwguF6X68vEoiWdgraLuLFXuARb6Vkgo0v9W09jbZ7ehDwtefYFeo4Heo45zmgcamua3u7emBw7OW1j87vO5nyvac12dZcn0T+t7dTV9qLnDMfsb8su8PVyEVK102kdt/YJFzMJmV9VUfMBi2qmBokSYc6oYy6khzK946h4IfZHFzftEe69D9jUUzxyJp6ZJXevtxVT7W1hz+XmiMnOPXw+zPPzt21RWo93Ghqq0AgEDgSbteX7hnDnx8I13uipVz/29IVGHXpIEloEkqxmNyPTAfL9e6uLxIdGi3tFLRNxIu9wCMtnRzEl/q2nnrfPT0IhJNupy5Goz1G0bqrG3rsLbFYS+M1HiKRUcXUcviPaajjqr2Gdbka+SjdPY6SHWe6DRfrNPwdBl5+FVveep2+F1kfjI5un8COj54ke8hyEju67pFO7/UJZX+fhqG6g9PyPlPnOfX+e+rh9wdfRnUihWhqq0AgEDgSTtcDpOesIz7tIPHpBfZtWpXrZT1YYhptHbmuz5t2ByCz95u5x0vnNqzLOesBMvv84LYzIRDXu7u+SHRotLRT0DYRL/aCJjiGkgXzS9sRd1+AjiJ3rG/r64OGvyH2vrarufvZevFpVKO+6Uu9dZms0pM7aaFPPcDu5rd1GfscBzf+w224mMUU4/Rg1L7fN6TnWB+MEjvudNkj3ff8eU0yA9t6qvd8/u+gzz0L1ahMKAQcqrYKBAJBKIgU18Nx3x/tRdXBgT75Pvpc7yrxnH+ud+eYULje1p4jW8+lvKaD0zp9RTfWL1od9HnmoXCocL2grSHpdDo/8la2XaqqqsjKyuKUu7ugjmt1xQScaPyCZqpPRR1XgaQyBiU5iKeQP8cHjb+/uJuSnRPJzPuKXmc/BATnQQNcf9kHGoro634HN13ArlWPgqJyXiEZkGQFxRxrX9R+wEr6X3SzT+fe8OInDJ5xqX1+G4CpPglZrWfHx485CbvDwI/od8Gt3o+rwLon1mExxdp7pCXZyKCrL+T3/yzHWJuKYolFUunRxFcw+JoLUMdVU7JzPPnfzrWP6APEJB2m5/gnAgpTs5hisJhi7A8kjtfmKsmhL4Qq5DQUbRUIPGGqt/Djg0UUFxeLDPBBQri+ZVwP4fN9eFyvYO3INwMNy311PYDZEMeml1cw/IYp9vOHyvWDr7kQgPXPf4ViikcdVwEoyGoDw2afRemu4Loegu9Q4XpBa8JX37duawkCwtdSNIHiGPLXmMI1s1m/aDW/LV1BRcHw49tbe4LXL1ptL83SHNyVbvHULqf9G3WF+bpfSudtVrc3OaDmeK+3clyeFsrzT/apVExFwXBqj/am5sgJTsvVcdVIKkPAJWhsPdJ9L5xjL31jrEtj/aLvMdS0Q1YbAZBVRgw17Vi/6DsK18x2W54oUNHLaoOTPG3X1hx5+vp5+Uso2ioQCAShIpyuh/D53h8HOPrSH9dLTUbsbUhYp+ApgOKz6wGqDpxE7bFcp/OHzvXfsemlVSimhgEHRZEx1LTn7y/+HXTXQ/AdKlwvaIuIUHyBS7wlB/E3vMnXkL9gZhx110bHL3tt+91+hSLaeoAHXn4lZoPW5/0A4jPyiUk6hlmfiMUUg2KfbycBCl1Gv0zupEc5un0iOz9+0m1Yl6/3sjnhYq7mL46dP5TiP86l8Ic5x+v8gtmQSEziUXqc8RSdhi2L2LlnLRFyKhAIBNFGuFwPwcsw7qmNNt+X7DodbfvdPrXLflyLzIYX/kf/i2chyRa/XK/RlqGYNahia6gv70LDOJqMKq6M3LMfQh1X7dH1vt5PQ3WHoLv+yNbJ7PvuZnu77fPrJz5O9pDlwvUCQYQiQvF9pC2F54Hr8CxZrWf07aNB8T+8yd+Qv8bzuH0NK7O33yKz4cVVDL/e2kZ3IX+ahEqKf5+GOr4SLGqv7SrPH8kfb7xF5gnf2OvSOl+PCaMug86jXqf3Ofc7tanmcB6b/7OMfhffwOHfz3e6vnb9P2HAxTfZf/YU1uXrvQxVuJinz8bxGh0fMAZffVFY556FOuRUIAgHIhQ/+AjXt6zroXm+b+x6cB3iH5dWQH1FJ9RxVSgWNWZ9itd2leeP5PfX3wUUNAnlAbk+o9daNr/6LhX7TnZ5fd6c7Mv9zJn4mHD9cYTrBa0VX30vRuwFLvE02musTfU7e64/PfPB6Akuzx+B7nA/yvcNJz3nV6cSNI7JXiSVEZCxGOOxGLUu2+WqB1gxq+lxxlMUrbvaKYRRHV8GmGnX96smbbL1jKtia9j50eOoYisx65OQY2qo2j/U6fo8JQHy9V42N7mQK7x9NsHIVByKHv9ARobCPfIgEAgEoSacrofm+76x6wGXvjfVpaKKqcdUl44taa2rdjX2PYqKxI5/oq/u6Lfr1XE1KIr1HoNCfOZeTLVpTtfnzcn++N4R4Xr/okCE7wWthdbfHS0ICNuXdkavtQAkZW9n0IyLkWSjU3hTbWlXaku7YtCleT2mr3OyXM35Mhu0x+XoHoMuzd6e/NW3AjL7Vt9KbWlXOo14k5yzHnQ5lzDvvDto1/drt+1yNw+waN2/rLVeHVBp6gG1y3sjqw1YzBpKdo3DpE9C234PIJPYfjcmfRIlu8b5dB/9uZfBxttn09y5Z+7yHwQDf+5ZKNshEAgEkUI4XQ+B+d6T62tLu9Lt1MUucweoYnQkdf7NY7tc+b6+ojOm+kSn/XxxfW1pV6vv65MBCW3mXnLOfAiTPsl+bF8Ih+/biutD3RaBoKURofg+0tbC8xxRFPj7i+aHN3kK+ZOkhh7TQEPId656kEObLkIdV4VZn4RiiUGSDahiqzHVJ5M9dBlmg7ZJaFnf6bd6bBfAwU0XsvuTh1AsGvu+MUmHMRtjUUxxWBwSzIDs9t7s+fzfFP1yeZPQf2QTproUuox6i15nP+C159jbvQwV7j6bT+Zu4OecBc0+/lZjDv+uuo4Hk19igGZvs4/niKLAlRX3YFDUZMmlFFsyiZGMvJF6f5N7Fsp2NGbLil18u/YvAMaP6cvA8/uE9HyC1kOtRc8F5UtEKH4QacuuB9j9v39zcH3LuB4C870vrs+berfLUPKKfSM9elOxyKx7cg2KEuOU8d1avs4MimOgq2fXH/j1clRx1vto1jfcR2NtKp1HvEPuOff55Otw+N7b5zJ+TF9uTX7Vr2OunvC4/f8jxfWhbosjjq4HuO2ZaR62Fgic8dX3IhQ/ygl1+JBjuZDmJrnxFPKnbb/bfp5AQ8hVGj0gY6pLxVZORrHEWH+WQFbrKd11epPQMnd14B0TzySk72/Sm5uUtZ3K/UPp/49r0FdmseeL+Zjr0z3eG8cwMUNtptO2eefNI2vQx2x88VOvcxrDVUfV3WcTG+tmBx+otGipVeIAWFk3DhNqVtadSqZcAUCCVE+KrPN6HIsFZA/P4QXmLOqUWOYlvsHgmD1sNvTm0ZorKDBn0UNdHLR2CAQCQbBpiVBhxSJTUTCc3ufexb7Vt4Xc9ZJsCcj33lyv0uhdhpKX/T0WiymG/pe492ZFwXAMNR1I7bHe6cVeksycePmMAF3vfB/9cb23+xkq37v7XOydz8/Aah53s7drIsX1wWyLP9wyYgXfrj3B+4YCQTMQL/ZRTCA1Ov19OHDMIO8te643PM3JcjyPr3P5GpM76QES2u9i18pHnZbLagO9p9xNcqftHP59ehM5KmaNy3aZ9PHUlnYFYP/PMwAVkspAQrs96CuzqS7uz6jbTkYTb93P13vj6T6W54/06T4EY35bpLCsbjyf1Y8lUdJhOv6VtM2Uw22VN1GjaJkc9xMztas8HsNgkbmo/BGWpc0nxs3fQmfVUd5IvZ8E2XqPBsfs5vXUB4iRjEFrh0AgEASbgOuuB+j7hIxC0nqui1rXZw9Z4abD/kUGXn4lqd02O7XL0fW26QeVhUNRx5cTm3IIfWU2slpPes5aJKllXP/sdYso6vMDW1bs4usf/g6K74MyQvxM4LtGiuuD1RaBIBIRL/ZRjL+C9PXhwF25kJikYsr3now6voyEjEK/k9w07gEORVmS+PT92BLjOFw5WYNXoJhj3MrRVc/0vu9utk8/sBitQ9KSbMBQ3R5TXRKSyoy+sjOa+J1+JQBqvG1taTfK955MbWlXn+9DKBLjhYuZ2lV0VxXzdu0kKpUEAHRKAulSJTdqlzEhbqPT9q7u6ef6MRjQ8IX+ZKbG/9TkHIoCGsmMplFtYa1cH3A7BAKBoCUI5GW4Ob7fv3YG1QcHoY4vJy6tCH1Fp6hyPbjrXDi5yctwY9fbku1ZTLGg0lN7tDcZfb6lYt9oa8RBh50t6vqB5/dhIH04/cDDHu/Hd52bPxUu1ATi2Mb3NRiuD7QtAkE0IF7so4zmCNLXhwN3GeR/e3klpro0Mk/4ghMvu77Z4WBuz7N0hV9lSRzvScF3cwAJJCOJHXZTW9YVi0FLzeE8krJ2+vUy7BhKZzZY57NYjImo42rIm3Yn7ft9bX9I8CdUrvG2W99dQsmOs9j00ipQpIDvQzhRvGTq8PZAODFuA1uMufxgGGpfNlCzp4lczYrELZW38EzKMxwyZ1JmSQFged0ZgMTyujPorrL+LaTLlXRRH3PaRyU1NNRVm3xth0AgEISS5r4MN8v3+0ZhNiQCFtIGbSQ99/GodL2vvneVSR1k1DH19Jz0ANlDVtjnl4fL9ZHy4u7N5cFyPTT4/jbtO1Qo1mewYLne37YIBNGCeLGPMvwVZCAPB+7KhWgSD5M7+S66jHwP8C8czNUXa6BlSTzdE4sxHrDOsdNXt7f+LBvt98pffJ1+4E9ofONtT7xsFkXrLufvL/9tT84XyH0IF4pFZvToWO5XJCeZ2nAnW6djKPCnKYdkqdqe7GabKafJ7812U08KzVn8ZerJUt1U8s2dkVCwHbVcSeKu6lkoSPRUHeD51Ked9rElxvH0AOBLOwQCgSCUBPIyHCzfmw3JaBKPkDPhcbt/2rrrbZ0Cbdn13lweTNdDg+8fqPkXxZZ2QXW9v20RCKIF8WIfZfgryEB7yl1JLj1nnf2l3oZNdp6+CD2FBDZ33j40Hlm3lqQxGxJRxdaQd57zyLq/+Bpi78/ogKttEzvubpKcz919ePa6RQFdS6jYuDaDa++x8FdyT5cZZV3JtjGekt2ky1Uuk9wsSHqT13TnsM44kIbKnTJgYZxmE//QfkWxOcNlYpwCU0eXbfIl6Y5AIBCEmkBehoPr+5+dziFcb6UlXR9peHN5c13vLoltN9UReqgOBtX1vrRFIIhGxIt9FOKPIAPtKfdrzriXuXyeQgIdzxOXtp/6cv/m7QdyT/whlNloHUdXXCXnK88f3XR+2aRfWN3Hv0y0oaDCrKUOq3xf0Z3XRKYmRUYtWX8XfMk46ynZzeu1kz0muYlHTx3x9mO1l8pJUem4tmK+0z5bTb24pfJmdEo87aRyl23yJemOQCAQtAT+ei3UvheuD4xAXB8pOLrelcuD6Xpwn9ROjYnGORWa4/oUWSd8L2iViBf7KMSfl24ITIT+SM6VzH0NCbSdp++Fs8n/5g5OOP9m/lq+2G+ZOj80FFF9qH9ADw2NCWX2eeewQlfJ+RJCXr4uEEwWiesr76RaSSDJzct2N9UhCs3ZPmecbZzsRlEakt14SnIzO2EZz9deBFiIw0A9MRxTUrk6vuk+tUo8oBBPHTUkuG2Tt6Q7AoFA0BL463oIre8jzfW2e3J022RO+L+5yCrfKwY0Rri+KS3tevDse1AAJaiuF74XtDY8VIIURCoNgpzD0Oum0feCOZgNWnRH8lxu7yjC5M6/o0kotT8cuMMmuYxea4EGySVk5qMoVpnXlnZtkt3Vtiz/29tYv2g1vy1dQUXBcKAhJHD9otUUrpntdB6V2ojuSB9UMQb7eQK9J73OfBQUBVN9itt74iuy2mAXvQ11XHWT8LpAyJ30AH2mLkCSLU7J+STZTN60Oxk7f6jf9yHUmBWJWZXz0CnxTIv7HhUKOgf5qlC4UbuM51OfZrb2Q7frPZWRMSsScypvxaw0PLlOjNvAiZo9TtsN1OwhQdZjQeaKuM9YkTGfy+P+hwWZdcYTXe6TpyogXjL63SaBQCBoafx1PQTX9/EZ+RHt+qHXTaPr2JdQLBoO/3G+X8dpjHC9M+FyPbj2fZZ8DJCE6wUCL4gR+yjE357lQELM3M0js5hlNr74KandN3Jw/T89zuXrM3WBx5BAdz39vc5+yH4+X0vgqOPLOGnGRahja/n7i7sBDSndfgTJSG1p14DK6bQEoQorDDa2eW87TV0ptmSiIHPQ3IFc9X7WGwfYt3PMKBtoxllX8/TcJbmZlbCcN1MXkqmqAuAi7XecHvcbWqnO5T4lSioD1HtYYxRZcAUCQWQTyChysHwva3RsemlVxLk+ITOfodefA4pMbWlXKguHARLHtk8gtft6v4/XUgjXN8XdnHxX7j5syeSNlIW0UwvXCwSeEC/2UYi/NcyDFWKmWGTWP/ctdWWdyZ30IIkdd3idy+dJZMEqgQNQ9PO1TY5VVTSE31/7IKJLxgUSatnSVFq0vKabwneGochYUI4H+mwx9qKeWEAhjSrMksopo6w/GWddJcxxnBNXYk5xmeTmqJLRJMlNO1UlAPtMrhPjbDH1FllwBQJBxOOv6yE4vo9k18tqAwd/varJ8Sr3D4/oErHC9Q3n8OT6BKmeMkuyS3fXoKUdVfZjCdcLBE2J2FD87du3M3bsWDp16sSCBQtQvBTKfuihh9BqtU3+/fjjjwBMnz7dafk555zTEpcRETQ3xMwWind462TqSruDYu1tT+2+nqROW5y2dZS5t5BAx/A0U30qYH1gkGQzfabO80vMwTxWSxJIqGVLs6xuPN8bhhKLHhMq+/I64lGQyJGLeCdjIbcnvk2dEkuBOQtoyDh7e+LbPJ26qMn6xue4tmI+t1XexJ+mnkDDnLhrK+az1nAib6Tez+AYa7idLclNZ9VRt+22JcZx3Gdh0lIMitqnNgkEgpZB+D54NMf30eD6UByvJRCubziHJ9cvqxvv0t2efC9cLxA0IOl0Os8GDQN6vZ5BgwYxfvx4br75ZubOnct5553H5Zdf7naf+vp66usbkl4UFRUxefJktm7dSkpKCjk5OXzyySd06tQJAI1Gg1ar9blNVVVVZGVlccrdXVDHRWx/SNAx6NLY+9V8in+fhiRbUMzWxC+qmCoklRFTXTqSup6kjjupK+uKrNYz+vbRSBLUHM5j83+WNQkJHHz1RU4hgduXP+PU099h4Ef0u+DWgNr754fPcHRrcI7VElhMMVhMMU4PYqb6JGS13uWD2OeTfmH1hJbPiv91/XDerp1EmZLitDxXVcCzqQ3l93SWOGIkIxrJjMGiwoTannG28XpfzpEuVXJ5wv+CFj5nVFQYFfdt8ljKKUQ9/VtW7OLbtX8BMH5MXwae3yf4JxG0Smotei4oX0JxcTHJycnhbk5ARJrvheujw/UQXb731/XPXreIoj4/tGALrbQV14N7p4fK9acfeJhznjrB/vNtz0zzsLVA4Iyvvo9Ia3399ddUVVXx6KOP0rNnTxYuXMhbb73lcZ+4uDhSU1Pt/15++WVmz55NSkoKBw8eRFEU+vXrZ1/vz0t9W8NxsKRwzWwO/zENVUy9PeQNwGxIxlSXDpKJE/9xjcseaE8J+BzP5W+iH0/tLv97LKCQ0G5Ps47VUoQyYU8wmRi3gQFq5+Q0GoyUKylO91cr16ORzJgViblVc4iVnK/Dtt7dOVwlyAvmnDiNZHYSfeM2u0rkA+6T/AgEguYhfB8+otX1tuNFk++F653PEU7Xg3unC9cLop2IfLHftm0bw4YNIyHBms1ywIAB7NzpexmQ4uJiPv30U2bNmgXApk2bMJvN5Obm0q5dO6644grKyz0nVtHr9VRVVdn/VVd7ntfWWlAs1uR4ikVGURpC3lQxtTT+dUnqvJlT7hrkVua+iMxdeFrNYd/D02zhg6W7T8NUnwJIJGTkk3PmQ5jqk+yZegWBoyiwxdQbUOgsHSZZqiGeerehbY5Jcfw5h22eXh9VAclSjX1OXEvgqc2BXI9AIPBOuH0vXO+D6zuFzvW6I3l+fccL34eWtuB6cN9u4XpBtBORL/ZVVVV0797d/rMkSahUKq8v4zb+85//cMEFF5CYmAjAnj17GDRoEKtWreKnn36isLCQhQsXejzGk08+SVZWlv1f7969A72cqMJWp7Z833C79LOHLCetx7pGW1owVGWhim1eD7Srnv5Rt57Cjo+eQrH49utZuGY26xetZseKJ5A1tcevYwR7v56PxRTHse1n+9wegWsKzFkYFDV3Jf6Hl9Mf4/bEtzFLKhYmLbXPe6u0aCk2Z1BsznBKimNbVmnxPGrmzzw9dzg+GPjykOCpzbuNndlt7BLw9QgEAu+E2/fC9Z5dL6nqqSkeYHerjWC4fvTcMcSnF9jP7wvC96ElGl3v6mdXuGv3bmNnthh6sdvYRbheEPVE5Bz7u+++G6PRyGOPPWZf1rt3b3744Qeys7M97mvrqf/888/Jy3M96vvTTz/xj3/8g8LCQrfH0ev16PUNYTzV1dX07t27Vc67M+jSMNYlIUnw9xd3U7JzIildN1JZNJC+0+eSlvMTG57/GqMug/jMfAzV7THrk5A1tQy99ny3JXQCpTx/JH+88RYnXfVP0nqs92mfQ79dQP63czFUd7Avi0k6TM/xT0RcOZnmEI459ooCJrzPV1uqm8pn9WNJlHSYUKNTEtBKtagxUaNomRz3k8f6sb7MifOEWZG4pfIWnkl5BsD+/yrJ/VecpzZXKolIQLJUE9D1+IqYYy8IlNYwxz7cvheud+96U20aFrMasz6ZvGl3kD1kedDbJHzvmnDMsY9G16skpcnP7nDXbqOiwkAMMRjRSKaQuV7MsRc0h6ieY5+WlkZJSYnTspqaGjQajZs9GlizZg0ZGRluJQ+QkpJCSUmJk8wbExsbS3Jysv1fUlKS7xcQZRT8cCPrn/2BTS99TPm+UQBUHTgJlBj+Wv4sO1c+isUYT88JjyDJZhLa7QYkErP+tNeJN+jSmtUGW3hdbWlXpzq3tmXejp89ZDlpPZ1HGiKxRmy0YZtvJmPxOF8NYKZ2FbO1H6JCQadYw2p1SgIqFG7ULvMqRm9z4rxhC6HbbuzpczidpzbfrP2Am7TLAr4egUDgnXD7Xrj+JGfXm+LoNekeJNlMz4kPYzEmYKsT76uPvSF8H3lEq+sdfw7U94lSPRNifiVRqhOuF0Q9EfliP2TIEDZubEiiUVhYiF6vJz093eu+H330Eeeee67Tsssuu4wNGzbYf968eTMdOnQgNjY2eI2OYtqd8DVIFsyGBMx660ONYrE+VEnqOir3D8ZsSCD/m/nUHu1N7VHriKLuSB9+f+0D1i9aTeGa2c1qgy287relK+xz5Gx1bn05frAT8wis+DvfrCWS4jjiGFq3vO4MTKi5v/pq3tdNxISa5bWnew2n89Tmlr4egaCtIXzfcvjm+jgO/jqD2qO9+fvLfyNrdEBDnXjh+9ZJNLneFi5/f/XV/K7P5YPaiT6Hz7tr981JHwrXC1oF6nA3wBVjxoyhsrKSd999l8suu4ynnnqK0047DZVKRVVVFfHx8W5787/55huWLl3qtKx///7ceeedPP7445SUlHD//fczc+bMlriUiMWgS8NUbxV70boZoKiJ0R5FXxXvtJ1i0iLF1tJz4qMc2ToV3eF+mA3W/cz6VGT1EfpMneeyp9yfkiG9zn4AbYed5H87F6MuFbDWpY1JOuz2+I7YEvM0LrejO5IX9KkCrZ1Ki5ZaJQ7Aab5ZplwBQIJUT4qsc7mvY1KcLLmUYkumPSmOP+VjfN1+Wd14e2hdrWL93a0jlq3mXgBsMeVyW+VNHsPpPLUZgnM9AoHANcL3ocVv1094gqr9Q6kr64nFGIditrrAk4/9/T4Uvo8MIsH1tmN528fR9abjry51xHJ3zXWAdedtppyAfW+xCNcLWgcR+WKvVqtZvHgxV111FXfddRcWi4Uvv/wSgBEjRvD4448zZcqUJvvl5+dTXFzMkCFDnJbPnTuX/fv3M2XKFNq1a8c111zD3LlzW+RaIpXCNbM58OvlqOMr7KVt9FUdAAvWL8mGbzKjLp2in69xKoFjQx1f5fql3iKzcckqhs2aiiRbPLbFcdvy/NFOdW59Da+zJeaxZea1JeaR1e6nWwhc40qgvggTGpLizEt8g8Exe9hs6M2jNVdQYM6ih7rYp/P7Ol8OrKF1HeRSltVOxIjt97Ph99eEBkmp40btMrc9757aDDT7egRtAQXH70yB7wjfh5bmud45qNOVj/1xfePthe/DS7hdD7773tH1OhKOL5Wc/qtTEkiXKgPy/TrjicL1Ah+JbN9H5Is9wJQpU9i6dSubN29mxIgRtGvXDoAdO3a43adnz55UVVU1Wa7RaFiyZAlLliwJWXujjdxJTXvMQYU6vgxFkVAsMhajFnVszXH3WzDVp9r3l2QDkkqPxaB12aNpy7hbUTjMa0Ice3begmH28Lr49P3UlXW1h9c1Pn7jZbLa0CRDrzqubZQtCjYztavorirm7dpJVDrMN/MmTIDOqqO8kXq/ff7c4JjdvJ76ADGS0efzO4YEDtDs9br9EUsGVbjPWpso1zarzbZ1iuL79Yhe/jaEZOHkue/z85OXgBKRs9siHuH70NEc1yvmGMBCXFoRZn2iSx/743rH7YXvw0+4XQ/++d6V6yUUFIeXLG/h8+7aLWERrhd4Jwp8H5mtOk52djaTJ0+2S14QGO7mnblKQJPS5XckZAZcegOn3DWIE6bfDIpEUvafTtu1H/AZAy6dZa9DC94T4uhrGhLiuNq24LubMOmTyDnzQU785wynOrdO1+NQf1cQGgKdPxdoUpzmlM+5JmEVCdQi4TxapMJEHPXUE+tx7qWnNtvW2RILmRXJ6/U4bito/aTnHCQpq5T0nEPhbkpUI3zffELh+tSe6xh926lOPvbH9SB8H8m0tOshcN+7cr2CjISFjtIxkqUap2l0/rQ7QTYI1wu8Eg2+j9gRe0Fw8BQm55iAxtZjXnO4L6NuOxlNvDXErV3eD6TeNob1z32FJqGUuNSD1Fd0omLfSPpOv9Up/M1VyJ8tIY6pLhV1XCVj5g1Hki0ut60u7oek0rNr1ePojrxF7qQHXIbX+TtCIPCfYM6f84XmhgQiyQxQ/c1WU0MN6sGanZwb91OTcLpArsGfUQV/Iw4E0YdGW4cm3vq91GPc78hqC93Hbaa+wlpL3VgXi1EX7+kQAkFQCbrr04qoK+tKXUlPFMU53H3v13f67Hpw/WwgfB8ZtLTrIXDfu3N9rmo/z6Quchk+7+91CNcLGhNtvhcv9q0cT1J0l4BGX9kZTXxDAhp9ZScsRu+JalyF/NkS4nQZ/QpF6662t8PVtma9ddteDslzbOF1jRMA2UYIYpOL7dvFaMtDcg/bIsGaP+cNm3SbGxL4esr9XF91J8lSDe3lMo5Y0sk3dWaQxjmczp/5+/4kFmpOEiJB9NFr4nq6nbIVoy4WSW19eUnvdZBRNy9Do9VT+OOJ7Ph4XHgbKWhTtITrbT72x/Xuthe+jwxa2vUQuO+dXV9NB7mMYksmpZZUl+HzvvpeuF7giWjzvXixb4X4KkVPCWgcv4R9SVRj2z57yPImCXGSO22htqRXk3Zk5n3rc/Icb9EAnUdae/wFwSFY8+c80Vi6E+M2sMWYyw+GofZtDIqa02M3eTyORjJzQGnv08OJPz3svowqXJOwCklqXsSBIPrY8fE4qg61o8/kdcQm1AEQk6CnvkLLtvfP4OCGfmFuoaAtEC2uV8dVu9xe+D78hMP1QEC+98X1Wrnevr2vvheuF3gi2nwvJi21QnytESurDXaB21DHVSPJJqc5be62syWvcZwD5xjyF5N4FFAo2TWe8n1N21Hww2yfa9HmTnqAPlMXIMkNiX1M9alIspk+U+cJyQeZ5syf85XGdXMdy830URWQQC01JLDd6L2uru3hZHCMda6g7eGks+powPP5ZmpXMVv7ISoUdA6jCioUbtQu418Jn9jn13nbVoi+9XFwfT9Kd3d2Wla6u0vESV7QeokW1xeume1X7Xnh+5YjHK5XFOcpAP743pPrIbD5+8L1Am9Ek+/Fi30rpLlSdAzp8wXH7W0hf30vnMOYecPpPPoVUCTM+qbtyBq83L7t0OumuU2eY8NVAiBfy+MIIgN30j1oyuSaygXUWOL4V8In3J70Dl3kI4DEe3Vnen0J9/RwsqxuPNdWzOe2ypv48/iDha2H/dqK+SyrG++0n+1BU1E8JxZq/LASaBIiQbSikN7rIPrqeCoKOqCvjic99wDWUjgCQeiJFtfnTnqAmsN5wvdtCE+uv77iDrYacqhV/Pe9t44If3zv2Kk0IVa4XuCJ6PG9CMVvpfgT9gb+z2lzt33PCY9z0oyLiEs5AkDvSQ+jr8ri2J8NdYjTeq4ja9DHbFzyKaNuHYsmwVqyyFstWlcJgNyVxxFEJu7C2G6pvBkdCXSUjvGc7mKn9XtNnZoV5ubPfD5byOCTyc8yt+pmnk5+ximx0EFzJn8Yczlkymgyv05RYKsxt0WTEAnCR1J2CepYI5tfm0Tp7m5k9Clg8FVfkJRdQvUhkdld0DJEuuuzh6xAscjs+Ogp4fs2hDfXf6ofw7iYTWHzveP0AIBbKm+hwpIoXC9wSTT5XrzYt1L8laK/c9rcbf/7ax84ba8oULV/SJN2lBcMR3ckl5ojJzgl+vFUi9ZdAqDGSfwEkYs76WowAhLd1Uc4U72elXXj0GFdX0sC6XhPoueJiXEb+MOYyxqH+XyuethtPfOf60+m0JzFd4ZhTvP5Hqy6kl+MJ3Jr1S1YjtfOtY0GVCtaVFi4J+mVkCYhEkQGuqNpfL9wBqb6WABKd3Xn+4UzsJhUYW6ZoC0R6a5XFNtIv/B9W8Kb6xVU/F/8GtrJFayqOzWovj8jdiOv6s51WtbY946j8ApQYM5Gg5G7kl4Trhc0IZp8L17sWyn+StFTlts+Dllr/d3esR1JWdsp2Xkae/53HwXfzfE7060viX0iEwUQ3bg2XCXNURzEucPUjSqcQ/AGavYwPjbwMDdFgd+NfQCFLvIRKpVEew97lWLNclttSeCD2gmYUPPfujMwoeZ7/WDu1r5KO1UllRYtdye/wad1o1lWN5FyJQVoGA24PmE5p8b+EdIkRILIwWJSYzE5K9Qm/eAhvjsEnolE12f0WsuRbWex8+MnKN19Goc2XiJ83wbx5nrbyHyWfIwKS7J9m+b6/k9jT2pIQEstnVVH7SPqFWYtdVgz2i8/7vgPaiegIGFGRX/1HrJUZRSbM7gxcTkD9buF6wVAS/g+eN8bYo59K8UmxYxea4EGKSZk5rvdx985bb5s79iOwjWz2bXqMWS1gZrDJwCuE/24w1tin0hERmFJdjFyBM7DCReOSXM6SseQsNjD8GxJaLTU2pPqJEs1bDXm2JPX+INtnt9G4wnHE91IdFIdY0bCKnRKPH8ae9rn5N1ZNZstx2vjlivW0NOtpt7cWzPTaW7elPh1DHQxv+7s+PUhT0IkaENIFk6+/T2QLN63FbRZwuX61B7uXQ9QVTQMiymOHSue8JjYzx3C99GPL66/UbuMemKb7XvHOf3v150JSHSRD3N70jvMSFhFrRLHK7VT7fPvtxl7AfCHKZctJuv/b280F1+4XtAiBNn1YsS+lSKrDU0E6CnsDfwP6XPcPi5tP9WHBlCe77y9Yzucev5rMwDPIwWtgQFxenrEGOkfp2drfVy4mxMRNK6bO79yFluPv1AD5KiL+MvUk9sdytk8VH0VFeZkn8rUOWKb56dFRyx6akmwRwXoFQ1rDScyK3GlPWSwTIk5vqetz1MiUarn8oT/2cP4HB9WxPw6QahIzzlIUlYp6TmHKPu7s/cdBG2SlnZ9fPp+aku7cuzPyVjOn4ussrhsh7PvMwHh+7aGN9cP1Oyhl/pAk/J1gfjecU6/UbG+2hQpHe1RAWfGrmOmdhUD9X/zdu0kjGiO79kwvmlCg4pa+zQA4XpBSxBs14sR+yjCVVmYYOKY5daXrLWO2/c68zEkCcz6ZLfbQ9vIdJssm8lSG8lSGzk/uQqNBOcnV9mXJcttu1fXsVyNosAhSzuSpBpyVYUkSzXkmzrzesr95KgPUWzOIEtVSn9NPmZUPpWpc8RWmkYtKdTiXJrmJu0yZiWuBFxnubXReG6e7WHl9sS3eTp1Ebcnvk2dEkuBOav5N0fQptFo60jIrCAhs4Ie435HVlvoPm6zfZlGWxfuJgpaiFD6vjmuH3rdNLqd8hKKRcPhP873eB7h+7bte2+u32bKoZNs3aa5vncsQ9fY9Tdql3FD4kdoJLNH14Oz74XrBaEilK4XI/ZRgmKR2bhkFcNmTUWSQxOaaQulU8X6NqdNHV/OoBkXo4rV8fcXd6NY1KR234AkG6kt7epyDl1byHR7WWol5yVXU22RUR8PyRsYV8/z2YdJki2srEpiSVl6mFsZPjSS2R6y1rhH35aE5qiSzjf1w11m1fU3Y66reX6NX9Yde+ZrlXhMyKixEE99kx5628OKmF8nCDa9Jq6n2ylbMepikdTW7/n0XgcZdfMyNFo9hT+eyI6Px4W3kYKQE2rf++v6hMx8hl1/DooiU1valcrCYYDEsb8mktrdmgxP+F74vjG+uP6gpT091MUs1Z3VbN/76/qOcil7zF0ByJX3c1hxHpEXrheEilC6XozYRwn+1psNBFltQBVTy8YXP0WxWH81PM1pK/p5Jpte+oTflq6wz6Gr3D+Yza9+4HYOnb8jBdHIkrJ0ni5Jx6xIJKmsok9SKZgViadKMtqs5F3h2KMPDeLsrDrq1AOvU5r2wPtaBsdR5LY5fDZ527A9dFwe/zmxkoHL4z6z/jfhf0166L3V0RUIAmXHx+PY9sEZKIpMTIL1dywmQY9ikdn2/hnipb6NEGrf++t6WW3gwK9XsX7RamffFw7zOGde+F743oYn1wNB8b0/rr898W1uSvyQGAzEYuSmpA+bjMgL1wtCRShdL0bsIxh/680GimPvueMDhWNZGlf4m10XojnTrX98VZPE4Lh6xifV2pf9XhfH1zWJPh6hbWTWdezRt6GV6+3/70sPvCscf6fdjRQ4lqaxPXRoJBOnxv5OgqxncvwvxEhGTo39Q/TQC1qMg+v7kdl7P52G7bIvK93dhYMb+oWxVYJQ0xK+D9T1IHzvieb5XrjeRiC+D9T1CbIeo6LinbSFKEjESEZ6qIvFiLygxQiV68WIfYhpzjy5wjWzm/SQ+5NV1qf2WWQ2LP4C3bHu1JZ2dXqgqC3tSm1pVwy6NLf7u5tDlzXY9Ry6aMx0GxgKJ8brqTDL7KiPocIsMzC+HnzIlisy6zbgSw98Y8yK5JRR19VIwWspDSMF0NAz79hDb+uZFz30bY1w/90ppPc6iL46noqCDuir40nPPRAB7RJ4orlz4kPt++a6HvyfMy987/mXQrjeGX993xzXO/6/o+OF79sS4f67C43rxYt9CFEsslOom7/kTnqAPlMXIMkWTPWpgLWHXJLN9Jk6j9xJDzS7jRUFw9Ed7c36574N6IHCcQ5dcuff0SSUUrFvJBteCPy6WwM9NUbiJQsPHc1kdnEWDx/NJF6y0FPjvSfYMbNuW8fX5DWO4t9u6kmhOYu/TD2BpuF0ZkViQfX1yEROGbFQJ8YU+EgElJhLyi5BHWvkjzfPYt3Tl/DHW2eijjWSlF0StjYJPNNc10Pofd9c14Nr3x/dNhmLue26HgL3vXC9M774XrheEBRasevb9rdxiAnGPLlQZJU16NLsPfRF62aAoiKxw05A8vuBwtUcOlN9CrojeSHNBxDpFBk1XFrUmc318QD8Vh/PZUWdKTJqXG7fljLr+iM2b/PywCrv2RW3c9CUSbE5g5V14zChdptRt/HDQCja7Q8Wc/NfCgTBwbHsTLjQHU3j+4UzKN3dDYDSXd35fuEMdEc9j6YKwkew5sQH2/fBdD009X3Xsb5lx2/t+ON74Xr3ePN9tLvebEa4PkJoza4Xc+yDTLDnyQUrq6zj9oVrZnPg18tRx1egmK3iqa/ojNnoXHfVlwcK2xw6i1lDbWlX4tP3k9JtPWV7Tg9JPoBowYiEUXH+gHSK+y/ztpJZ16xI3FJ5C8+kPINK8m5PX+blbTPmUGDJ4trKeSS7yag7IfZXpsd/D+D0MJApVwCQINWTIuuC1m5/KNjaz+GlwHP9aUHw0Wjr0MRbR3kcy87UV1jnxxrrYjHq4lusPRaTGovJWc2m+tgWO7/AN0IxJz4Yvg+V68Hq+6HXnwONs+Nvn+AxO35rxx/fC9e7x5vvo931P/8s+5XXQhBc2orrxYt9kHElUluom6kulc4j3/IrpM7WQ97v4hvI6LWW0j1j2L7sBXRH8kjsuNOnYzQuneMuCQ6SGVlTTWKHv31+oJDVBmS1gT2f/9vNdX+EUZdB51Gv0/uc+32+7rbGkrJ08g0aZqRVkna89EWSSqHEJPNUSYYfSfciG8ce9AGavQEfp9KipVaxPpy+U3c2INFRLqXWEo/OoYZtulTJjdpl7DNnc23F/IBL6QSr3a7av3b5ePtLQenpyyg2Z3h9+BAED1FiThAIwXY9NN/3oXQ9WH1/8Nermlx35f7hwvU+IlzvH63J9R8/p0KxqO0dgML1LUtbcb14sQ8ygWSO9UQwssq6yn6bPWQ55fmjObJlmn07STIz4LJZAT1QuLtudXwZYKZd3698v+g2SvMz6UcmjmILpAfdFW/Vns3X+pFopTr7sY9a0pEaJR1xzKjbXVXM27WTqHQqpWPiBu0yznSRdTcU7baxrG68vWavfqv1abqiYAQv3TCc+opkn2r2CoLDjo/HUXWoHX0mryM2oQ6wlp2pr9Cy7f0zHDLUto3s1QLfCLbrofm+F66PDoTrfSdYrk+XKkmXKvhXwict0m4bjq6XfrZGcdg6ADfq0oXrW5C24nrxYh8CXIk00HlyksqAulEGWXWc93BdT2GCxroUFAXK945GkvUolljreeIqiU/bj0GXRkbuWkbdNgaVxvcOBFfXrdLUY6pLb9Nh+b7TkFm32KgmS2NyyKwbvV8yjmILpAfdFbGSEQWJGiUB5XiqEDNqrPdKIY0qzJLKnlFXklyX0rEgk60qa7F225ipXWV/+KivSQasD8fx2lJu1C7zWs5PEFwOru9HZp/9dBrqpuyMZOHkue/z85OXgIcpNYK2RTBdrygNEXCOePN9MFw/eu4YJJVwfcshXO8rwXJ9V1Uxf5p6uRyJbynXl1VY22/rABSub3naguujs9URjrtM8f4m5GhOpl1PpXN+e2klm19ehaGmHbLamrVVVhkw6jJYv+g7CtfMRrHI/P7ae0iyyed2265bVtcBFsCCvqqj07mDVaavNdKcTPqRzEztKmZrP0SFgs5ptFzhRu2ygIQ5U7uKm7TL0GBqsm5a7He8k7GwSUZdRYEthlwSJR09VAdRY0JB5uNa14l3QtFuRybGbeBEzR6nZTmD/xCiDwsKmX32oyhQfTi9SdmZSEi0I4g8WoPrAVQxOn5busLn7PbC9c1DuN6/Ywbi+m3GHJKkGnqoDpIo6fjLlOM20V44XJ/Wc51wfVho/a4XL/YhwFWmeLNBi+5Inl/H8TXTrquHCE+lc/Km3UGfc+8iJrEEs8Ea+mU2JBKTWELvKXeRO+kB+7nL9w33+YHDdt0D/nENedPuRBVXge1XLNhl+loj/mbSjyZcic0xdC4QJsRuaDK2EYORf2k/A5pm1M03ZVNOEjVKHGWWJFTHHxS2mnpxW+VNXFsxn2V140PSbld/o7aavdqUCvtLQf4fJ4pyOC2IRltHQmYF7fruQxOvR5JAdzSVnavGoI7T03HwLhIyK5wS7SRkVpCQWYFGWxfgWcUH3FpoDa53PP/6Rd8K17cAwvX+EZDrlWT6qfay39wRiwKGRiPxjX3fEq7PzFSa1QEoCJy25PqIfbHfvn07Y8eOpVOnTixYsADFh7+A6dOno9Vq7f/OOecc+7qffvqJwYMH07VrV5577rlQNt0+Ty6j11qgYZ5cQma+130bl6exhdXZlhl0zmUQPPX0eyqd02n4+6Tl/Oy0LqXbBtJ6/uJ07n2rb6PmcB8Ob53c5Nyerjtr8HIy+6xxeW6Ba4xI1DYK/dEpMsYoDs2zYRNbslRNH1UByVKNPXQuUPaZsjCgJp5a+qgKiKcOI2r2mRpq3mrlenuW3UolERUWzo/7HhUKeqxz6uqId9sz79ju3nIBMma2Gv1rt1mRmFN5K+ZGWZNtNXsv/Pfj9pcCQ128U81eQWjpNXE9p9z1Fide+jUmg/XBL73XQfLOXYs61sigy79k1M3LSOt10L5u1M3LOOWut+g1MYCsxhFQOzcSiVbfR6vr03LW0q7v103OX1fWncNbJ7s8v7vrFq73H+F6/wjE9RIK5yX8xGzth8RJJmxTHNyNxLeE6994w+DUAShc33K0JddH5Iu9Xq/nggsuYNCgQfz000/s3LmTt99+2+t+v//+Oxs2bODgwYMcPHiQDz/8EIBjx45x4YUXcsEFF/Ddd9+xbNky1qxZ4+VogSOrDfbkNzbUcdVN5s65wlNYnavQNk89/Z7CBF2tK909znrulz+mfN8oAKoOnARKDDv+u4i/v1jgUfi267Y9gJTnNz9EUdA6sInt9sS3eTp1UZPQuUAwoSKeehYkvcHTqYtYkPQG8dRjQmXfptKitYfdfVY/FjNqDpo7kKve73Qsdz3zju2+KtE6OqBT/Hv5dldL11azN3foH4D1peCOD660jzoIQs+Oj8ex7YMzUBQVMQnW7+eYBD2KRWbrexPY+v4EFEUmJkHvtG7b+2cElD3X/zC/1v+FGc2+j1bXV+wbScEP1vNvemml3fcgs+O/z7J+0Xf8/eV84XqB30SS61fWncoAzV6ffN8Srj/jDOtLnq0DULi+5Yh810OwfB+RyfO+/vprqqqqePTRR0lISGDhwoXceuutXH755W73OXjwIIqi0K9fvybrli1bRseOHZk3bx6SJDF//nzefPNNTj311FBeRkD4kmnX1/q5nkrnAC7XdRn7HIU/XYfFaJ17pFgaQsNKdk7gyNbzvJbxsT6A5CGr9fS/9Dqn49ccziMpy7cyfYLWg01sCbL1S9MWOhcjBT6nsIe6mDfTnI/5RprzMd0lxbFmwHWdeMeRZKmGR5MWEy8beEV3HhZU9NfsRIW5Sakax/19zbLbuGZvXGJtk2WC0HJwfT8ye++n0zDXyXQ8rfOFgGvntoIkPr7QVn0fbtdnD1mO7mgu5XvH4pywTQJZz5Et09DEVwvXC/wi0lx/W+VNVCpJaNDTU1VMsSXTpe9b2vXquGrh+hYmYl0PQfV9RL7Yb9u2jWHDhpGQYE1iMWDAAHbu9CyITZs2YTabyc3NpaKigkmTJvHss8+SlpbGtm3bOPXUU5GO/yUOGTKEe++9N+TXESjeMu36Wj/XW+kcd+tqS3s6nduKhNkYR4/xT9C+35cYdGlO2W6bPoBoSO3+I/Fp+6kt7UpS9nZG3XoKm197l+HXn4ski1DUliP8mXY1krmJxLRyfciP6ZiR1rH0jYSFabHfcXXiZ2w29ObRmisoMGfRQ13stP+K+tObPCzsMPVgXtUNTtlyzYrELZW38EzKM6gkJaRZdgXBRiG910H01fHUlSYTn1HlnEzH7Trf/qYCrZ3r2ONf9nfn4FxqBNKWfR9u1w+66nI2vLiKmkMDHVolgaISro9KhOsbuz5FqiYGA/9OepXBMXvc+l64vi0Qma6H4Po+IocBqqqq6N69u/1nSZJQqVSUl7svm7Jnzx4GDRrEqlWr+OmnnygsLGThwoUAVFdX061bN/u2ycnJHDrkOTxCr9dTVVVl/1dd7b3EXLDwlmnXfbIcCzFJh+l11kOA5zBBd+sklcEh222jsBBFoujna1yGCboKK6zcP8QprLDm8AnUHu3tNUFQ6yLM8YhiTq/LpDhjYv7g6kTXiXcc8TVbbuMQvFBn2RUEj6TsEtSxRv548yzWPX0Jf7x1JupYI0nZJR7X+UpDCKD3MD9bgp/gJ/GJXMLt+7bqelltQFGgriSHxp5SLML1gRFG3wvXu3T9SZpdvJt2L4NjrMvd+V64vvUTSa6H0Pk+Il/s1Wo1MTExTstiY2Opq3N/kXPnzmXlypX07duXvLw8HnjgAVauXGk/XmxsrH3buLg4amtrPbbhySefJCsry/6vd+/egV+Qn/iSaddVshxt+10Yqjs0S6a2c/e/9BrU8ZVYS9lYUSyxILnOduvuAQRJocf4J+g04k2fEgS1JmQUlmQXI4dR9q2hdEdzcZXMZ4eph9McUMfEO41xly13eMxf9jl9jiF4tmUjYrYHPTuwIPjojqbx/cIZlO62vgyW7urO9wtnoDua5nGdPxxc34/S3c698K7C/GwJfoKaxCfCCbfv26rrbeeXZDOSRoej61GE6/0l3L4Xrnft+u2mHOIlvdN27nwvXN+6iSTXQ+h8H5Gh+Glpafz1119Oy2pqatBofC8FkpKSQklJCXq9nrS0NEpKGnpdqqurmzxINGbu3LnceOONTvu0lPC9hdVBQ0+/Or6M2JRi9JXZVBYOdzsHz59zj7p1LL+9sgLFIpHacx0V+WPs65Oy/3Sb7TZ7yHKObDkXQ3UH+zJNXCX7vr2dop+v8RhK2BoZEKenR4yR/nF6ttbHtdh5mzXPxyXhD+9rDrakOPMS3/AYiucOx4eFLLnUPkfvg9rx/E/vPgTvnNifXO7nai6/IHxYTGosJmcVmupjnda7W+c7nkIAG34Zdnw8jqpD7egzeR2xCdYX25gEPfUVWra9f4Zf8/2ihXD7vq263nb+E/8xg82vfkhKj3VU7hOuD5Rw+D74rodo9r1wvcATkeR6CJ3vI3LEfsiQIWzc2NDTVVhYiF6vJz093e0+l112GRs2bLD/vHnzZjp06EBsbCyDBw92Wrd161ays7M9tiE2Npbk5GT7v6SkpGZckX/4kmnX1tOf2v1Xao/2xmKRsJisDy+eMuu6wzZ6KasN1BzuS11pN/peMIe6kp72MEF1fBk1xX2bZLu1le3RlXSlcv+w4+2tQB1fjqk+hZ5nPuiyxm5rrHObLJvJUhvJUhs5P7kKjQTnJ1fZlyXLoU+W4n8voIcRhigJ7/OUgdmWzMdbKJ473GX5nRi3wWMI3sS4DU32q7U0LzuwIDrxJ8zPnx7/1kC4fd/WXA/Ovjcb45EkC7VH8oTr/STcvg+q6yEqfC9cL4hk/A3pD4XvI/LFfsyYMVRWVvLuu+8C8NRTT3HaaaehUqmoqqrCaGyaXbN///7ceeedbNy4kS+++IL777+fmTNnAnDOOefwyy+/sGbNGkwmE4sWLWL8+PEtek3BxtbTf+Jl19Nn6gLUMXpsH6e/MlUsMhsWf05tSfdGNXVnYqpPIueshzjxnzPod+FNTcIEwWHO3csr7Q8cABaLhKGmHTWHBrqtsdvauCy1kjc6H+L57MOcGGdN7jIwrp7nsw/zRudDXJZaGfI2+DXPx4vIoyG8z13tWBsayWzPpmvDU+h9Yzw9LLgL3ZsQt5FOsvN+AzV76KAqJ0s+5u8lCqIc/8L8Gnr8Kwrao6+Od0rw09oQvvdMMF0PTX1/cP3lKBYNpvpkcs56iL4XzKHPuQuwGLXUHBau90S4fR9M10Pk+164XhDp+B/Sb/N9HBUFHYLi+4h8sVer1SxevJg5c+bQvXt3Vq5cyf333w/AiBEj+PLLL5vsM3fuXPLy8pgyZQp33HEH11xzDXPnzgUgMzOTRx55hKlTp5KTk8Nff/3FHXfc0aLXFGwce/pdzcHzVaaK0lAf99dF3zolxKkp7ousNrBr5WMUrplNes464tMOEp9e4HQM+5w7lRHHBw51TD25k+8m77x5HhMEtSaWlKXzdEk6ZkUiSWW9wCSVglmReKokgyVljqNQvtyAwG6Sr72ArkQeLQm8bL8/7mrHBgtPDwuu5vRtM+VgskjMrZpDrNQw8rbd1JMicwf2mLs1PoWglWMxqZuE9ZnqY5uE/oFDj/9bZyKpFP54e6LfSXyiCeF7zwTT9eDe97Kmjr1fzWf9otVUFQ1j1K2nsOOjp1AsDY+JwvXO+O77yHQ9RIfvhesF0YI/rgeb7w0Y62JZ98xFASXta0xEzrEHmDJlClu3bmXz5s2MGDGCdu3aAbBjxw6X22s0GpYsWcKSJUtcrp85cyZnnHEGu3btYsyYMSQnJ4es7cHG2zwdx8y68en7qSvrapepu/0MujSMtSlse/9l4tMKARXaDtswVGc5hdE51tQtzx+J7mgvqg4OJK2Hc4iXu7I9XUa+S81h9zV2Ezu2vjq3X9UkMTiunvFJDQmbfq+L4+uaRPvPMgovZBdzw6EsLG7ms/myjXvcz/PRaOs9zsvrcfomuoz6K6CSHS1FuTmRBVWzuCvpdY+1Y0ON45y+QZo9/G60zulbbRhGoTmLjYa+dFMfBtzXuBWEg3DPI3V/fluPf3LnoyRllaKYNHy/cAYWk6plm9iCCN9bCYXrAfTVGfz++nsMuORaitbNwJXvzfoUVDGNfH8kl4rCYU6+F653xpvvI9n1xrrYZpXoagmE6wXNI3JdD1bf//HWWQy5+jPScw7ZR/ib4/uIfbEHyM7O9joX3h9ycnLIyckJ2vFaAsUis3HJKobNmuq2HqxtDp4rmWo77HQp/MI1syn65QpQVOgrrPdYX9EFs9E56UtK102kdt/YKES/acIeTw8cviQIal0onBivp8IsU2xUk6UxMTC+Hsc/cF8S7TQnGY9t1G/za5Mo3d2NjD4FDL7qC5KyS+g8YrtXkW/74IyITuD1ou589luyuLXyFvt3Zjhqx9pC92IlA7Mr5jI/6Q0eSXqBd+rOxoSaV3TncVRJQyvVYUblsp0j2BzydgockCycPPd9fn7yElAkWk76x//+nc7vHDTnLhnWzpWnAIEmw4oO2rrvQ+V6gN2f3U/t0d5senml/bc9UN9rEsqF653w7PtId32kJ+xsTa6f5uUcgiATNtcDKCApPrm+6+g/XXa6uRvl90ZEv9gLGsLmGveaO+IoU0VpkKkkG9n44qdODwoGXRqm+iQ6jXiTsvyR6A73w2ywJguy9tybUcXUoG2/h7qyrpTtGcexv85Ek1DhMdOtpweOxI47nZIBgfUBobXSU2MkXrJw/9F2bK6PZ0hcHf9uf4wBsfWUmK1/co6Jdo4d75nTHQ951B7/rFxtg6nUpzbYRv1sIUGOvYC+ijyz9346DdtlP2a4E3hVWrTUKtaHHjNqQMKABqNi/b3UKQmkS5XcqF3WYmVmNJIZjWRmqzGHQksWsyrnkeSQObeaBGLRU61osUmlcTu3tEhLBTbsIam9DnLCeT+6lG7QcXjAcAyJLfvbOYQ20kfPBKEjVK4HjrtbwmKMQzFbv0OtJer8933W4OXC9Q648v097Y8xPL6OIqMmYNerDFU+nT8Yrj+4vl9E+b61uh5GtEhbBVbC4nqw+37HyrFhcb14sW8moShn4ShkT6PkNmS1AVltcOrxV8dVHw+lc35QKFwzmwO/Xo46vkHcjel/6XVOwu5+2vMc2ngZRl0q0DREH9pqT71riowaLi3qTO3xL5Df6uO5rKgzV6aVc25yDdUWGfXx+XS2RDtJsoWVVdbP/LzkarfbULIISHR5Xke8lfXwLnLfS3a0FMvqxvNZvXPJmcZhi82tHevP37Pjw8fKunEoyPSQiyhVUp0y56ZLlXSVj7DL3CNo7RT4h6uR8N6T1pGUXUrW4N2U7OwW0pHw9v322c+VPWi327JUkT561tYJtu9b3vXOD7WSbPbb9xZTjHC9A658/22Nlgc6HGuW6yv2LqcA7xEnzXc9RJrvhesFgRJu12u0dbTLKyApu5Tek34Ji+vFi30z8CV0LhBcCdmXerDWHv88jmw7h+TOW1w+KHQ7dTHaDjvJ/3auXdwAkmwAlR5NXA3pOWsBZ2HXlfZoMqfOMWGP7YHDkdbeU+8OIxLGRllbdYrMC2UZ/G2IYUZaJWnHe+iSVAolJpmnSjLsc/LyDRq329zc937g8SC00rPIPYX3VR9qF4Tz+89M7Sq6q4p5u3YSlcdlakaNhIX2chl1SlyzaseaFYlbKm/hmZRnUEneExm5evg4omRgUJy/Vk9U7+FPs6hxG07sveO1Mfbe8dRuh5FVCif980sOrD+Bvd+MCGqou+MDxgnT1iCrLJx46deYjz+Ep/c64LJ3PtJGzwRWQuH7lna9Yo4BLMSlFWGqT0RWG/z2vXC9M658/3xZBrub6fpJZ/0L+CEILfT+0h5pvheuFwRKOFwPDb7PmbCeziN2IMnW8wKk5x5g1C0foEkwtIjrIzIrfrTgGDoXTOyZZ32oB2urK9swJ07DX8sXsemljynPH3m8nc61bl1l1m0/4DNOvHQWFmOCUzk7dVw1ksrQpjLdhpKvapL4o845Y2bjxHq+bNNcvNXatIb3XeVHyY6WwVXJmQGqPbyW9pC95mygtWP9zbg7U7vKZW1bE2riqbdnzv3D1NtlbVxR47blsJWFUsWYiIm3vpTIausXmNmopn2/Ajd1nwPHsca0Rmt9wZdUFvv5YxIMrstSOZW7C075G0HzCYXvw+H61J7rGH3bqfS7cI7wfQiJFtcD6I6m+lmiK/QI1wsCIRyuhwbft+9XgNmodjpvTLwBlcbUYq4XI/Z+YqhNxXC899uX0DlfcNWb5y7zbOOyNq56/GVNPaa6VDiewKNxKJ1joru41IPUV3SiYt9I+k6/1WVInbf5820bf0PVvCfW87xNcPA0Lw/AYpYZdcsKpzlJjUt4hAPHkjMdpFIOWzI5pLRHURpqzsZITeteu6NxiJ2/GXcnxm3gD0Mua4xD7ctUWFiQ9BqDY/aw2dCbR6qv4L7kpfTVFAbcTkHzcdU7DqCONWGqiw0g/M3z376rUDu5UVe6q975SBs9a8v4GyrvC41932KuTyuirqwrdSU9nebnC9/7Sut0PZKFUbd8aHW9A+H2fSS6fosxlx8MwvWRTku7Hpx9r441NVl/ZGuvFnO9GLH3k6K1M1m/aLVT/dfGveT+oFhkNr74qVOtWHAua+Op19xVj7/FmNAkVM7xQcEm7r4XzmHY9efS94I5mA1adEfyUMdVN9nXNn8+o5dzyF5CZr5f19rakFFYkl2M7IeEbYl2HjqayeziLB4+mkm8ZKGnxujXNg0E9gDgrdamu5q3ocSXESFbyZnbE9/m2bRF3JHk3CNuqznrK8vqxnNtxXxuq7yJP4/33tsy2V5bMZ9ldeM97m+ySKw1nkQS1tq2SdSQLFUz6PhIw+CY3byR9gC56gOYFYk5lbdiViS/2ykIBg294/oa5999v8PfJAsn3/4eSI1Dsp1/iV3VmDYbVR57520P4pE0etZWKVwzO+S+bzHXzzqPfhfeZHc9IHzvI8L1wSUaXa8osM2Yg4yZ3rJwfWTTEq63nscRV77X18S2uOvFi72f5Ex83OfQOV9wF97nKOSh101zevlujKtwO1DcPig0Fnd6L8/iltUGe6IcG64eCNoajiVqfMWWaGdzvXVujy2xXpFR49c2gJcvHP/RaOtIyKwgIbPCqdSWbZlGWxeU87jCUYSesJWcGRzTINPXUx+gs+poQOd1F2KnQuFG7TKvZXRWG4ZhRsUF8at5OtX68KHHOfTOJnZ/w/8EwURxCEk9E4tRg7E2BotZwlAb63f4m8uHYZd/jw0PGFUHM1AsYKqPYd3TF7sMiQXvD+KClsOfUHlfcOX7SHI9CN+7Qrg+eESr6wvMWegU6+c0I/Ez4fqIpQVcDx59b9DFYjFL1vMaNfzx1sQWdb14UggAX0Pn3OFLeJ8/WeYb15CvLemJqT6J/pfNJDO3IZSu5nAeSVk7nZLfhCoBYGslWTZ7LFGjs8hUWVRu93eXWM/fbQCPZbMCIZylthxFOECz1+12tpIzjmjl+mad21WInadMto4hfb8aBgASf5lyGG3+kyxVKYuSnyJTVdlkW3fhf4IQc7z0zC/PXsD3C2cQn151PPztHCr3dySt5wFOuvxrr+Fv7mrM27LdJnYsbfL36BhqV57ficwT9jHwsm9Jyi5pGhIriEhC7vv4sqC73lbXXrg+cITrxzX7PK6IJtdDg8NVmOmv2csmYz9W1p3K1dpVPJL0AslSjdN2IFwfNlrI9ca6WJKyStz6/o83z6R8XydSuhUz+KovMFQntqjrxYt9ADSWa11ZV3svuS+ZL33NhOtr5tnGc+KO7TyNv5YvIi7J2juUkbuWUbeewu+vv8ewWVOcpO5L7VxBA5elVnosUbOyKoklZemha4CplITMCsD9F06gmT5butRWc+e8BQvHuXy+ZLJ1lSHXFtJXo2iZHPeTvfffl21HsDnk19iWsT0Up3Y7StnfnZvMOT32V45P0nX9MHyAUbe8jybBQF1ZUpO/R4MuzulcR7fl8v3CrvZzhXseq8A7LeV7R5rjend17YXr/SPcrlcZqoTrg4y/rgfffe/LdtNcn0IQJELr+oPHM9vrObghjxit3qvvHTvvWzLiTrzYB0Bzk8vkTnqgSRkaV7XhfUFRmtaQb5f3PSfffrJTj3/N4RPQHcmlonAY2va7g54QqK2wpCzdY4maptlsg1sHVlWyiFPueitkPe0tWWrLnxfkUGKbyzcv8Q17EpxHa66gwJxFD3Vxk+1nalfRTVXMOw6leGw1bG/ULnPq/XdVtqfxtltCfoVtD1963B3x5QXb9cOwAUUBU73Gnvne29+jeJmPLiLJ9/EZ3l3vWNe+ZNfpaNvvBoTr/SXcrm+/dzl5d/1PuD6I+Ot6gGsSvDscfHM9jAj5NbY1Ws71ekz1aiQJ2vcr8PlvMhy+Fy/2AeBPmLw7mhveB86hdeq4pqP7Bl0apsqOgLPUNQmVFP8+DVVcJdLxOpy+1M4VWPmqJonBcfWMT6q1L3NVokZG4YXsYm44lIUlSMI3d7yf7YsLQ9jT7r3mbbDwTYShxzaXL0G2/v16y2RrViT+Vz+GAZo9rPES0ldp0TJAs5dc9X7WGwfYl/dWF9Jfk0+lRRuCKxKEKtTU1cNwxb6OxGdUE5diHXEK5ciXoOWJJN//tnQFw2ZNdVpuG913FfK/5/O7qa/ohCq2CoTr/Sacri8+4V8cfKReuD6IBOJ6W717byH8wvXhoSVdf2RrL0r2dGmxSJdAES/2AeA4b82Gu9A5dzQ3vA+8h9a5CwGUVEZQZMx1abgrkyPwhC9lbJwT7mytjwva2UPZ097SpbYCmfMWbPydy7fd1JMCcxYlllSvIX3L6sbzaf1YFCTAgjVfqYX1xv5sqOjHlABD8f35nmiLhC7UtOnDcFxaDaW7O7fIyJeg5Ylm35vqUpE19Zjr07G5SbjeH4Trg0W0ur7QnMV2Y0+vIfy+uD6QUHzhes+0pOvTcw+w5Z0zWyzSJVBEVvww4U8mXEcMujRqS7tSW9rVaRTetsygayiT4Cmjb5cxLzf5tvB3BKGt4qlETbJsJkttJEttdEq4Y1uWLAej7In1C8dYG4OigMUi+Z3p0x0tXWrLcc5bH1UByVKNXZiRRKVFS7E5g2JzBivrxmFGTY0Sz78SPuH2pHe4PmG5UykeGzO1q7g4/uvjP8lO/70k/quAwg9tWYVNlrZme/9+KVyVnvFNwO7P05Ac5yzWPX3J8cz2BjL6FKGvjvdYxk7Qdgmn72WVkca/i8L1viFcHzyi1fUm1LxXdya1Shz/SviEe5P/w+2JbzfxvXB9MIlU1xtJyj5mf+GPVN+LEfswEWh4n7+JeFyFACZ12kJtSS9QZCSVHm27v6mvyKZ87+hm9w62hd5FW4ma2uPZa20lagyKxNXp5aFNuGMqpV3ffajjDNSVa0lOMICioEmop13ffVQUZgWcUAes5TcaJ/kI5RyhQOa8hQNXcwRjMfBa7RT7HEF3IX0Xxq9mv6kDa42D7cvGaLZwQfx3AbXFNoowq3IeL6U+ikqKHKGEjOPZbn9+8hJwkTXaNQGEmro9j3Wfxsl4Snd1Z+NL5zHsulUtNvIliD7C6XtZUw91wXc9tH7fh9P1KkOVcH0YcOX6vaZOqDHynO5iJput+QBc+V64PghEsOu/XziDhHblLRrpEghixD5MBFor1t+6uo4hgBrtUUChdOd4KvJHASCpjOir22PSJ2GsS/M6guAJxSKz8cVPUSyt+9fKiGQXvQ2dImNEYklZOk+XpGNWJJJU1i/hJJWCWZF4qiTDB9F7/uJWHXuMIdd8gqJAYvsKACQZJJWZIdd8Qt/zvw9pDdpgE+xataHCVQ3cWpxr4Npq2DZGjZmd5h5OIxU7zN1R07Ctt1ELV6MIxZZMfjKcRLE5o9XP33NbS9YD7nvcS9zu460+vau6s1UH2rfoyJcg+mhp30vqOqwusWCosubZCabroW34PrSuB0++77DnHeH6MODO9WrJud69K98L1zefSHa9qT6WmuLMiPd96/1GbsVkD1lOWs91TsvchdY5hgCOnT+czqNfASTMBmuSHYshEUm2kHfenYydP5SEzPyA2+U4B7At81VNEn80ysTpKuFOY2QsLMkuRvb0ci/FIUmgiTcgqxu2k1UKkgzZg3fTa2L0lDLSSGZ7Ihsb7l6Qw83EuA2cqNnjtMyXOYL7TNaRitsT3+bp1EVNwvgsZu8PyMvqxnNtxXxuqbyZbaYcABRknqm5hJkV83lNN6XVCV+jrSMhs4KEzAqnbLe2Zd4ean0NNfV8nnLa98v3+KDh7gGgJcvbCFovgfj+xH9cQ960O1DHV2J7zAum60H4HgJ3vfXbW/Hoe0WOFa4PE4G6vsCcRa3FvevNZoTrXRAtrofo8H3ktETgM/4k4mkcAth70sPoq7I59udk+zba9rsCnm/nKhOvKKfjW8IdR2QUXutUTEeNyWMCHltW/P4XrUZ1PAMoWD93Y52av1acdnxeUfAz27YFPIWWBlID16xIPKO7lFdTHiBJZU3S0zgTb8HWfg4PyK6Tcs3UrqLQ1JE/TH1w/FzNqP6fvfOOk6usF/dzzpzp20s2W1LYVEiDxBBaIEAIXMoFLnAVwoUfqKhXRIEgAnpFuHKVCyooxnaRBJEExIAoCtJLIIQUsumBkGSz2ZQts2V2+jm/P87O7MxOn53Znd19n88nys7MqVOec973W7Dg5g3v57DLrkFpGTRYDLTabaqhprG2Uz75IKfd9jRGu4eeluj+9APpIS0QpMNAfN8/LH8grgfh+2gyc/1jNc38uq00YcG9YFV84frckG3XA1TLx6gytDPH+AkQ7fr33pMTFsAE4Xrh+oEjZuyHIekU4ukfAujpLqVj38ko1nYUiwOAjv3zcbZEF+NJpajJ/rduZt0jr7HhN8/h2Hcy0JcDuO6R19j/1s0DO9hhSKKCO/0JFuBZZHdSa/SnVICnad0JEKOYit9lpn1vLbbKNs648ymQ1KjXCOITLFQT0GKbO5gjGG80Phbb/PUcCIxln1ob8bgfAy1qCc2Bct599vLQBXJr09i44XY/LP410wz7Ix7TkLFKvogQwZHCjjWLaFh1LpomY7LpMz0mmwdNlWl4+twB9XBOth2jzRvqS28q1AdkghcaZ96zcljNlAmGN5n63tNdSvunp2fN9SB83590XA+67xfZndSbfPy/EkdS3wvX54ZcuB5gd2ACjYEqtvvrQ4+Fu/7RRw0RBTCF63WE67OL5HQ6R0E1hoHT2dlJdXU1Z353HIplaMdDVL8J1W+KuGH3uwuRFU/SnL2tz/yEo1suRzZ3IEv05u2pKFYHAU9hqBiPpsqsX/4C8792KZKcWBqHNlzF3leX4e2qCj1mKjxM/eL/HZWVd41oGCUtIjfPLql4NQlf2AhskRzgprJ2zitwompg6n15dwACSNhlLaoAz0sXvs+H1y/jjDv/GLFNSQJVBV+3FaPdjSTBul9cQdsnkZVCBfHZ4pvE9zq/yn8X/YpZxk+jnvdpBnyaEhFO6FQtmCRfRDhhh2qnR9NnYH7rvIx1vpmcYmzgS70ytknuiAI9HruEu7sQxeLAbNFwO4q42PJOlLw1Df6f47/oUAvwYQw9fpbyEd8ufiqr5yKfmPMff49oLdP04XQ+/sMFOd+O12nGZI8MHXV32Nj14ukptrbJ/Uxaj+rhqvblNDc3U1RUlNNtjRbyyfWQue+3PfsTjnycXdeD8H046bjeLqssLeng/AInsgQ+FYyy7ns/EgX9fP+zrz6C4+xnhOtzQLZcD8l9/yfX2fzTcwoFkhOpuACHQ0axOJAMPlRnmXB9GMPX9ZBPvh96awnSJtNCPJoq4zw6lckX/ReKyRMqxgMyksEbUYwnnfy5dHIARwOJCu6Es7Skg/MKnHhUCSXsqQIDFMkam1yWmAV4JIOK36Og+iR8TksoNEyWAVml61A5kqyllaM0GtG02IVqnnedFXosfDRdIbUcwWCO3O0dt7C1d+S+wT+J2ztu4SuOu1jtWhxRoMfdrYe26q2p1Lgj8sFZBCtuiqQu6qTDgMY7/pNGcDscbZBay+jb8XsMaBpoKijW6Irlfpc5NdGHFeERCAZCpr6ffundmIoOYTBmz/UgfB9OOq5/ou4Qp9hceHpniY29ixUYwCRpMQvuCddnh1y5HpL73iz5Qq53OPQ3PVgAU7g+nGHqesg734sb+1GEY9/J9BydSuHY3VFiLpu0lorpr6fcMzec8BzAorpNGG2toRxAQXyCVXVdmozc77d6m9vEd45UxVyuu7mCN39wI69996sc2zEh4jnF7MNarudpD/dwolwSDMVb1ZP8Jjz89eGhe2qM33BNi11V16nZkLXIqrqxCvRMmruZxebYBXrqDEe5t/A3BCQDdxQ8ya/Lfsy1lpdQkXndOzILWGVS7XYg29nw239lyx/Pw+cy6xfPvah+GZ/LiMEc3RM8FplU9hUIsknnwRPxdVdSULUr4vGBuB6E7zMhvIK+VY4+Ue857TEL7gnXD5xsuF7TYqerJPJ9eMecWK4vrV8rXB/GcHU95J/vMyqe53K5aG9vp6amJuLx7du3c8IJJ2RlxwTZIV6xm86DJ6JY2rGUNuLpqMXx2Snse/Nmmtal3jM3SDAHcMYXvk755Hdp3XMG21Y/hvPIdArG7hzU4x1uvNxdyFyLm8WFPaHHPCqMMQaIFdpz4Uunsnh173dM0zjl8H6cmhGHYqPC14XRqGIy9uUodclmXimawdZrq+HayAu8bHJb0f/x2nkP5mz92SbYH/bL9hc4TmnmyZ4L6QiTcpnUwTfsq0NVcLf69Ndv99czy/gpXlXm8+3/w+rSuzD1hq8GNIlbO27lp8U/ZYnlQz72TeFN7+dC2/RJCueYPwr9HSzQYy92YCj8DFfbePZuns0tjqn8rOSnUT1rjVKAKcpBHi16CE2SaQ6UsycwAZB43zszFFJok9wUy85cnr5BI14vWdVvyOl2KqYeiAjVa944lYPrpyfsV2u0uzD2jvyHV9wdzkV4QPh+OBHL947983XXlxzC3VE9INeD8H2mxHJ9h19Ck6S4BffO/vvd/KTzizl3/Zwrpg308PKWgbo+oEl8q+NW0IjwcjLfz1b6qugHXV9RoeG1bA4VwLyle75wfS/DyfWQ375P+8Z+zZo13HnnnZSWlqJpGo899hjz5+sjSF/60pdYu3ZtkjUIBpP9b93MwQ8iBd6+9zQCXjtIKqXHradsyoNsW/0YNfOepWDsTva+ugyfswTQQ4ZMhYeZdul34oba9a/EWz7lXU5bdgayEh3eMvKJlWeTKPdGY67VjaqBTwOXJgESVilAvdHHXp8paolX390OQL3Ri1Tt4d6jlWx0W5lnMfPDqqMRo48fdCj87NNOYPvADy0Bt95ektP1Z4PwXLjwULwv2V9ginKAdb5ZodfOMe7hZNN2mgPlBDSJ/+66MfT6CtnBP90n48XI3z2nc6n1HaDvAmK7v56ZyqcRVXUbA1V0aza2+eqZbdKlHAy3+/fv/Tf73X+kdc8Z7Hjmt+xXS0MXFf0xSgH+4jkzlJ/v7/0J39Y789Ct2WPm7A1XUq12m93taJRNPqiHbzZWYi3tpmzKQT7+w/kJLzQGWtk3HxG+H17E8r3mN6MpXpxHp1I+7VUcn52WsetB+L6P9F0/2+qhKwAFMjhV8CDz0NEyvjemJcr33/rVN0P/nWvXv3pr8mUe9/6CbY+9lva6h4Jsun6nfzz7AjVIaBFejuf7YslJo1rFJv+0UBX9oOtXP+HlJ5svp3XPGWx9+tfsV83C9b0MJ9dDfvs+7Rv7H//4x7z33ntUVlayceNGbrrpJu644w4+//nPo4lYrLxjyoX3Y6+KFHjAW4ip4Cj15/04JPCgmAvG7oxqkZMsf05WvFH5fooldtuukUywjc3XD1Wj9so91mPh1Bt9GCWN37cXcV1pJ6vai1la0sE9h6to9BmjXh9Oo8/INY11oRy/TW4zSMRovaMism6IKFgXlGQwFK9DK8SIh3pDc6i1zaqexfzNsxALHnrQR143+6bwTcdtOLECEqtdiymU9BHzf7oXhC4IXBYTTtXCdda/Md+8k4e6lrIzcBx/dC2h0vAMAIWSkydK7mPz5H3seGs81rIDTJi1lT0fzg9dVED0qPxN9heYaEg+8yDIjMKaFkx2vTpu0/oT6D5cmnT0HvSKu52HKpl28VrMNj3P1WTz4HbYaXj63DSK8OQPwvfDi1i+BxmD0cWUf7mPmnnPhQrvZeJ6EL6HzF1vlVSedhRzQ1kHf3CUsLSkg/aAwtLGOrxxKrRDOq4PfidHah52amTD9R/3cz3AH3rO53zzB1jw8nfPaSHft5oK6VatXG/9K5v902lUx9KtWVnvPZ5xytGQ6+fM+T49r+muL56wjrY95wjXDyGZuh7y2/dpX+37/X4qK/UDnjt3Li+//DKPP/44//M//4OUqMFjmmzbto2FCxdSW1vL3XffndJFxOOPP059fT3FxcVccsklNDc3h5678sorsdvtoX8XXXRR1vY134lZ7GbSuxECDxbjEflzmTPL4gn1pU30WJAiOYBfg9uaxzLd7MMowUyLh9ubx9KlyliTVCjuX7hnSYETBfijoyis9U6Ax2ubkbNegGT4ES8XDiRMePmvwv8Ltbbp0SycZNrNf1hfwouRoNjdWHpFr5/PDq2Qh53X8rDzWhoCkwD9AuKnXV/AhZnfuP6N2ztuoVHV6yXsDdSFcvr+7D4bm+zhjT98PtRCqnH79NA6+uf+hRMrZ2+OcY8Q/QCwVbZRNuUApsJufC4TkgSTFq9HU2U2P7kkZk2F/jStm0Hr7sjq1K27xw3Lm3oQvh+OJCtuJ1w/cAbi+hkWb4Tr/RoYJC2q4F44qbleZZLRw/Ka3Pj+RtPNPHzrGj5+LncpfdkiG6539XM9wFb/ZB52XssPnTeyxT8Z0F39M+cXcGPm965/DeXvmyUvP3NeHeH6H/9YCbm+s3FuaHnh+sElG66H/PV92jf2lZWVNDQ0hP4uLy/nxRdfZOfOnWzdujUrO+XxeLjqqqs46aSTeOedd9i5cydPPvlkwmXWrl3Lfffdx+9+9zu2b9+Ox+Ph7rvvDj2/adMmPvzwQ5qammhqauKZZ57Jyr4OFqnKNl6Bj1QFnk7PXEFfH/pqxccVRZ0YJfh8UQdzzC7mmF18vrgjbq/apSUd/LaumQerjzDboo8azrG4+XH1EX5b18zSko60tn+6rQdJgtkWD9WKj8N+A10BmRqjP+bFxmgkliRPNO7iqdLvM9ekPz7XtJtFpo+4v+uLrHGfFXP2pe+nU0JCw4QXDT1sy6nZUCSNb9lXcYt9dcKiOgAX3/xbpl16N5Kshirkx3pdOMGcvSKpi2mGfRRJ3TT4J4mL8gFw0vUvseDmP3Py117AXKiPwJsLe1jw9T/zuZte5MRrX05hLYNV2XdwEL4ffNL5Dg/E98L16ZHPrj/kV/jPprH815hjcQcXssWr727n4VvXcM7BB3K2jWyQfddDcGDAjgt/bxs6p2ajUHJznukDCiRXyPU9mg0DkV1u/vd//SHXBztVCNcPPtlxPeSr71O+se/q0kOtfve734VG8IOYTCZWrFjByy+nejIS88orr9DZ2cmPfvQj6uvruffee1m5cmXCZfbs2cMjjzzCOeecQ21tLddeey2bNm0CoKmpCU3TmDFjBiUlJZSUlGC32xOuL5/QVJn1v3wRTU38dsV7XToCD+bPlU9+F+jLn7NV7M3eAY0ggm1sfl5zOCTsk6xuHq4+ysPVRzkpTOI/rznME3WHQhIPr5RbaNB/CAoNGgFNitn6Jtn2Z/XK/ESLi8dqmvl9XTNjjWrci43RSCxJbvNPwipFXgh9veDPfMP+DLKmEQjLWJJQkfr9aI+R2jnN1BDxWHBEPdXR9lizbIlG5YM5e3cUPBmaeXBpZvYFqlM7EYIo3nvoWho/0IvBSb0/ofr/SzS+fwLvPXRtvyWi5R2suOtzGVn708/nrLJvrhG+HxpSdX2i16bqe+H69MhH18+xuPlFTTNP1B3i5vI2agbR9xc9fDwP37om7vMP37om4t+Mr5+bk/2IRzZcrxB97k43NTDfFFmTYI5xD98qfEa4fpiQvuthOPk+5Rv78847j8OHD1NbW8vYsWNjvubUU0/Nyk41NDQwf/58bDZ95GvWrFns3Jm42ur111/PpZdeGvp7z5491NfrITEfffQRgUCAKVOmUFlZyfXXX097e3vC9Xk8Hjo7O0P/ghc6Q0GqfWbjvS4dgWfaM3e0EkvYZhm6VYluVcLU+w2LJ/GXuwvZ7IosELLJZYnZ+ibV7RcYoEjW0CDURu9Ei5vH+l1sjEbSkeQSy4dMUg5GPDZT/gQNCVCxoNcvOKaVsMWrX0BMNexHJsAW36RQi5xURtuDs2z2YkdKo/J1hqM8UXJfxMzD70vup85wNAtnafSy9ekl9LQVRjzW01bI1lVLIl8Yp2+t82gpm1eej62sm7JJh0KVfZ1H47cPy0dGk++Ho+sTvTZV3wvXp0c+ur7QoGGWdCOdZPUMie/738AH//Vn7GXzcrof/cmG6wPIgIpMAAMBJFS2eCfR4JtEkdSNGU/I1aqanuuNtlbh+iEkZdfDsPN9yjf2c+fOZdGiRezaFZlfs3nzZi6//PI4S2VGZ2cnEydODP0tSRIGgyHpzXiQ1tZWHn/8cW666SZAl/5JJ53ECy+8wDvvvMP+/fu59957E67joYceorq6OvRv6tSpmR5ORnidpaGesgcS9JkNf13/frTOlvF4ukuFwHNMLGF/0GNjXU9kq4vYEtcr5ToCMjvcJhwBuV8RnMy279civ9wFBo0iWWWjy5zS7MBIJR1J+lWJjf5pFNIdEvA+VW/5da35JZ4rv4vrLH9DRcaJlTsKnuQG+4uAHl63L1Cd9OIiKPPgLNu/f+/BlEbljVIAmxw582CX3Ril0RuNkR1UrKXdaCr43QqaCtbSLvQClH3071trtLuwVTiwlHQz/rStodY3tgoHpgJXb0/c4cNo8v1wcX3/14b73tmi+97rFL7PJfnoerMELg2UsCjyfPT9hS+dOqiz9um4XtNgr78GiQBTDfspkrpp1MZixosJLz8o/DX3Fv4GMz66sdGjWbjS8ioBDFxpeQ2XZmatb3Zarj/h378pXD+kpOZ6GH6+l5xOZ8q/Kg888AC/+tWvWLVqFZWVldx33308//zzXHTRRaxatSprO/Xd734Xn8/Hj3/849BjU6dO5c0334zqpRuL6667ju7ubv785z/HfP6dd97h2muvZf/+/XHX4fF48Hj6vkxdXV1MnTqVM787DsWS+wrje176Xm/bmg58PSWgKSgWB5LBF9Fntu91ensbv7sExeIA2Ye/pxzF1sbC75yMlKQQW7oE23gIADSeHteESdJCFWq9GoDU7zGJqxtrCa9YW2/08pPqw9wXamPj4ntjjnFb89iYre5S2f4Ekw+zpGHo9/5sdZv4ZnNuwrf+dvsOXq+7O/kLhxEvu0/mUecX+KL1Bf7N9hYbvVN5oOt67i58InSxALDLV4dfM1Bm6Oa3zstY55vJ55RtfNH+FwCMkp9qQ1vo9U7VgknyIaNya8et3PDK13j1nd2ofhMXLB4f6ikcfJ0Q+OBRNWc3c298iZ0vnMZnr5/McYvXMf2S99n4+IW0fTIu1Lf2+Mvepmr2Xg5vqWfn82cy6bx11J68E5/TgqSomGwevD1mNL+c09Y3PaqHq9qX09zcTFFRUVbXPVp8P1xcH/naPt8bzA69fa1moO7UJ5h60X1Z3T/h+nDyy/XVRj8yGl5NokKJvMbLpe8zJV9b5n3mr2ZZxy14MfJA0XJ8moH/6bqe7xX+H5OVptCNdXOgjLZAEYrkZ7XrfNb5ZnKKsYFrbP8ATcMmexK6/uOOCi5+aA6q34Ri6eb2n14e8Trh+sEjkeuPfDw1ok/9cPN9Wu3u7r77boxGI5dccgmBQIDFixfz9ttvc9JJJw14h8MpLS1l+/bIHJbu7m6MxsTtvwBWrFjBu+++y/vvvx/3NcXFxbS0tODxeDCbY/dJNJvNcZ8bDIJtaz75x12g6W9TrD6zsdrb+N0lKNY2kDT8rmIc++dTety6rO2bpsqsX/4C8792adYHDIYjwTY24cL+/pijIEnceyRS4v171fZvY7PBbU3a+iaV7f9w7FHCO3B6VRijBEjcZ1cQ3v/2fc9sQGKbfxKnBrZSbWjl5yUPUyFHhja+5Z0X1VpnR+A4vtP59Zi9Zu2ynou5xTeJ/YFq9jWcgKxsjZpRC75OkArZ+Vy37JzA6//1RTwdeojeZ68uoPmj6fhcFqZe+H7CvrWtu+sorGnLu9Y3mTJafD9cXB/+2oj2tZ4S9M9/gMoTslP3IIhwfST56PrvjTnGJx4jFUrf4FS++r7qrnOpuiu9WXtph4PXznswJ/sT9L2BADOUvWzwnxDqd/+j4scolrojZstfdC+M2Ubve51fScn1a9cGYraLFK5Ph9y7HpL3qc9n36d8Y9/c3MzDDz/ME088wfTp09m9ezdXXnll1iUPMG/ePFasWBH6e//+/Xg8HsrKEocUffTRR9x55508++yzVFVVhR5funQp3/zmNzn55JMB2LhxI1VVVUMq80R4naX43YWUTFyHrER+4YtqP6Zi+usRj9XMezaqH60k+0EzAAb2vfF1NFXCYHZiLT2IyZ5aiGM8wnP7sjlgMFyJJez/aNRbYHRohtBjsSTuQ8LX7zGnlt4sUfT2LXQFJEoMGo0+A8UGDQ0Vq6RGXWwIIknU/7ZLs3NJP3FDer1mwwcOnnctwo/Ce89eTvHJH+jLObI76zoqkFROX/Y07z10NaT53elPwGMm4AnzgqQy78t/5b2Hrk6pb+2c//g7tfP7wtfTb32THxfiwveDQ7quB933rbsXcWxbeAs/CVCE63NM/rneytLGWn5f14Sq0ev7ABpSXvr+ooePz2zBW+GlC9/P+g1+PN/f5rgFJ9E36gN1/aOPQs+U8YCeHiNIk0FyPaTWp35gvs+d61O+sZ85cyZTp07lD3/4AxdccAH//Oc/ue6662hqauL222/P6k6dccYZdHR08NRTT7F06VIefvhhzj77bAwGA52dnVit1qjR/CNHjnDllVdy2223cdJJJ9HdreeYFRQUMHPmTO68804efPBBWlpauO+++0L5ePnI/rdu5uAH12GwOPD3lOsPSn7QZFp2LmHfmweZetH9odcHi3FIigvNr/+Q+Jx9lYzb955B+96FgMq4054IhfWlQ/ACBIjI7TMX6b2DFUvXgC8ihiuxhB2UfDjpSjzT7dcbfRgljf86UsH7LjvzLU7+Z2wL3zpUQaMv+SxYJlz08PH8bNcjOVn3YHI/Hcx9agu/eGA6HUf0vEmnZqOUDsrlDr5o+0vM5ZZYPuRj3xTe9H4u9FisSrexLiQ++3gmgc3P4XeVIDf+ldN4O0dHNzIJz39r+6Qu+QIDWHfTuhlUTD0QR+Z9rW9crUVYyzvDWt+kIPAsXrQMFOH7wSFd14Pu+9bdZxGZm61/voTrc0u+uR6gSglg6vX9epeVFyc04tE07jlclTPfDwUXvnRq1q8x4vneQICv257hfOuHUcsMxPXvvivjfOO5UIoNZKdl6GhhMF0P5M73OXZ9yjf2v/rVr7jqqqtCf5933nm89NJLXHnllTQ2NvKzn/0sezulKPziF7/ghhtu4J577kFVVf7xj38AsGDBAh588EEuueSSiGWeffZZjh07xg9+8AN+8IMfhB53Op0sW7aMAwcOcMkll1BZWcmXv/xlli1blrX9zTah0LyXv0OoBJqmYLC0oQUs1Mx7NuL1wWIcs6/9Mp6Oanb/9V5UX3h7HxmD2cGUC++PCOtLh+AFSDC3D8CxbwEbfvNcVB6gIBOyN3qnj+qPC43qjzPpeVvTLT62em1Z2UYsvvWrb+Zs3YONPPancKQvAuakcwp4+3WN7f56Zhk/jXp9ePX7armVZrUiVOk2PD811oi/u7sQU6GTaZd+h4tv3gFvTMv58Q13wvPfjlu0KVS8xu3QC1b5XGZ8TmuiVWSwbv03tWzKwZgyD7a+2fj4hbTunkD5tH3MveHvFNa00HWoMsEWdXJ50ZIuwveDQ7quB933siFAzcm/pXnD5wm4i8OeFa7Pf7I7Uxfu+38r6sQowRPtxez2mvHlQfRPNsnVNUZ/36tI1CitMV87ENc7HDKmwkBfis2u7BYiHYkMneslfC5T3Jv3gfg+165Pq3heLPbv38+//du/sWHDhmztU4hDhw6xceNGFixYENVLd7Dp7Oykurp60ArqAGxd/QhHG/pa+lTN+TPTLvk+suKJyNFR/aZQMQ6Abc/+NCIsP7jsjKtuG9D+HNpwFXtfXYa3qy/s0VR4mPrF/5vxRcTIJ7nEZTQeq2nm64eqUTMWceR2ahUvFQb9hv6eMS2UKyotfpkHjlYA0BIw0OTPnxC9fELTYO3/riXgs2ApbsbtqEGhBLdb4hRjA1/qDc2zSW6KZSegF9+5s/NmvlOgF9bb6J3Kj7qv58dFv+A4pTlqG//btTRixD/4/Vx8xgmh4nmC+Bx/+ZsR+W/ZLF4Tb92KWf/N1VSJj359aYTMP3j0CpxHy5CVAH53X3ifYvGg+g2o/thj6IkK9EDyi5ZcFs+LxWjwfT67HiJ9L1yfTwyW6yO3JVw/MDQN3vvxB6h+M5aSQ/S0TEX1G3PueiBUPE8Qn6FzvcShDVOomrmv3837S3zw6FU4j5am5fuBuh5yVDwvFhMmTODVV18d6GpiUlNTk1JV3JGG11mKz1WI47NTkQxetIAJyeCl/dMz8HSXYrR2YQqTfXgxjmBYPgRAVvXff02mfe8pqCrIA7hOiZXLX1q/Vog+DqlKfJbFw3EmHzMtHra4LVnZznfHtDDF5IsI1iwzqDxUfRQJ2OM18rVDo++7lQrBCJiS49bSumsxsrEHd2/9nGC+ff9COcHWOsFCO3NNu3m8+H7McnTbk/4j/o0FpTg+OyVuH1tBNKnkv2V73ZoGe1+fy6cvLwjJvHXXRN74wf9jwc3P8d5D10QJPVz6sYhfoGcVRrs3Z9V1M0X4Pruk63ro871wff4wWK6PtS3h+oHhPDIdn6sUTTXg6RqDovjw+o187Jucdde3lEwIuV50mkiNoXL9p6/O45O/n8L2sJv31t3j6WkrxHm0GNWvpOX7wXR9VoajS0tLk79IkDL737qZdY+8hre7AknWpS7JXrzdFax75DX2v3Vz3GWdR6bj9xQiG13Mue4G5lx3A7LRTcBTyLpH/4mmZv6WBy8kjLZWiuo2YbS1ihuSBIRLvD9FcoBqxUe14uOK3vC5K4o6Q48Vyam3PYm1na8dquEfXXY0QO4ViCzp134vddmF6BNgq9jLactOZ/bSrzHt0rsxmNwEZ0f0/DuNb9hXh0SvadG9ZgOaxN1d/4kcoydq/972n//ugwS8dpxHpg/K8Y0UmtbNoHV3ZBhb+sXq4q+7fW9kqyhJAnul3qfWVuHAaNcvBIpqWyisbgv1uE2HHWsW0bDqXDRNxmTTPz8mmwejzUvDqnPy6qY+iPB99hCuHxkMlutjbUu4fmDYKvay8K55TL/sLiRZxevWw+ZdWLPu+iee8ArXZ8BQuL6gqg1LSTemAlfI9WWTmigc207JxKNpb2cwXT/gGXtB9ply4f1YK/bw2eu34eseA4DqK8BYcITjznmYupOfiTviZ6vYy6m3noms+EKh+WfceQptn57C1lWPDai6bXAmc8YXvk755Hdp3XMG21Y/hvPIdArG7sz4eEcSRXIAe29boHCJH/PrBXacqkynamBpSQeXFXXRpcoovWPtcyxufl5zmEJZ5fnOQpa3xa8Kncp2Hmqt4ESrm2pj34XDEb+Bn7RW5OTYRwrhETCxZq7CC+UENIlbO27lp8U/xSD1XfVu89ezP1AdMye//4j/lPmbOG3ZGciKB5ic46MbSQywWF2SdVcevx9NAy0gIckakgxlkw/2trtx0/ThdD7954IB5/3FKtAjySqu1pIBHoMg3xGuH74MlutT2dZv2kuF6zMk6PvBcP3ixWqY6wWpI1yfDuLGPk+pO3kVHfsXRPzIlE16Txd9gt6ysuLFVKDflIRXtz286SrQBlbdNjiTGbyIKJ/yrviR6keqEl/eVsZer5EbSzso7Q3LKTRotPhlHm4p55Xugixsp4QxSgBVA48mYZY0qpQAoJKlYJ0RT3DmqqJCo7x9f1ShnHCpjzccjmpv87zrLCpkB9CXp2eUAhilyFkag7lLhOalyUCL1SVbt6bKfPraSdTN342lWM+vNNm8uB12WnfXUXvyLsbM2Bezx238sLpYFyKaXpCv24zHUYC9qh2DMXsFggT5jXD98GSwXJ/atuzC9QMk6Hp7sYO6bodwfR4hXJ8e4sY+TwkPhbOWHcDVNj4UCpdqb9lsV7cNn8kMInpxRpKOxF/uLmSuxc3iwp7QY5tclpREn8p2FtqcyMBv2op5prOELxQ5+FJZBwttLt7psSdYe3700s4HgjNXT/zRi3T9I2z0TuV/uq5nvfd4xilHI6ReIDl53TsfOz2okj6TEi8nPxw1ILP+ly8y/2t68SyRf5cazqOlvHHvjZH57vfeiOqPbj81kHVbi3ui2t18/IcLqF2wLb28vzgtbgprWjDZXUiyBmoPasCAwahSPqUxhYsHwXBHuH54MliuT2VbLlXiiiKncP0ACLr+mv/+MVf+jysnrg8ECLleklXh+hQRrk8PMZSXpwR/ZE7492/yua9eztRLvovfU0jLrkURvWV7WsfT0zoerzM673HKhfcz7dK7kWQVv7sEAL+7BEnW222IljW54eXuQja7IotoxJa4xmyrB0dAZofbhCMgM8fqJrI/cebb2dBj5fMHanmmswSAVZ0lXH2glg098UcDZTSW1zQjp7gPI53gzNW55+oXU3NNu1lk3sh/d3+R2ztuYau/HtClvt53AiDhxYSzt71NrJx8ICJXdd+WGaGLdzUg882O2whowvbJUP1KVLEav9sctwJ9ZuvuCwF07KvC02UNhQCmm/cX3uImHOfRUl675yYaVi1G02SMFr0Ik9HqQ1NlGp4+V9zUj2CE64cvg+X6ZNsSrh84QddP+dxmIDeuf+89OeR6TRWuTxXh+vQQN/Z5SvBHpnzyuwB0Nn6OgNfCjj//L459JwN9I/KJiuzUzHuW0vq1EY+J6ra5JpnE9f+vN/qwSio/PFrBzc3VPHC0AqukUm+Mrq6ayXZ6kGlVI3/4jqkKPQm+9omKAI1GZMUbCkcN8vWC57jZ/gwGtCip32JfxammhojXh+fpgZ6rd7NjGQf9FTQHynn32ctDF+9bXl/IvkA1670n5P7gBEkJhgBuXnEBa39yNZtXno9i9lFY00KiC4EgRrteaM9W4YjIzws+ZrS7UAMyC77xHE0fHp+zAkGC/EW4fjiTyg27lgXXJ96WcP3AGQzXP/qoIeT6I1suEa7PI0aS60Uofp7SPxRuyoX3Y6/aqfeW7SkB9BF5U+Fhpl36nbjyThTmJ0KAckNQ4vcdrWSj28o8i4vvjTlGvdHHPp8x1Kqm0WfkmsY6enpDdTa4rSxtrMObcAQ3GDqnJdzOXl/qfWtTLQIk6GOJ5UM+9k2J6E87x7iHxeb1/MH1L6H2Nv3z9KA3V0+t5msd36FQcuL+WH//2z87jecePJsACr91XsYE5TAQ2UNXkC1SC0FNFAKYSt5f/BY3fWF3Rxom9Y7uN+WwQJAgXxGuH74kc3CwNd23Do0dkOtByorvhevTJ5uu590+17fuWYQmXD8IjD7Xixn7YUQmI/L9w/xOuOqbot1GjgnesG9062FwQYk3+owRo+Q+pJDogzg1GV+cL3YwdE5BZXlNM00+Je520mFpSQdP1B3i5zWHmW1xA32FeZ6oO8TSko50T8GIR9Ngq0/vTzvNsI8iqZsG/yQ+80e2t7mj4ElcmpmtvnqaA+U0B8p53rUIDZmJ8iFAwuPUwzYDnkICvaFlXdi4veMWvuK4i9WuxUN4pCMQSeX0O/4IUnR7ov6hsYlCAIMXAq27JwB9FwLOo32h0vFa3GiaxO4XT2X/OyeGRvcnX/ABisXLzucXsv7Xl/WbMRCMJoTrhweJXA99s+LTLN4BuV5GS7qtVBCuT59sur6jQ/8MBDyFoVoYwvU5JKHrIdz3I8n1YsZ+GJHOiHzwMVHddvDxIeELG4nP1ih58CLhkqLu0MXCFrcl4jVOLf2xumxU7R2pxJvt+tRfQ5tWzPcLfs3nzLvZ6J3Kj7qvJ4Ahor3NXNNufl9yP0/2/AsveU6nQHLi7/3ZPaKV49X6/QT3vn9OzUaZ1ME37KsjQvsEAyc8/63tk7BwuDgFb6LRR9VVvxKV49f/wgBit7jxucxMveR9jjtnY2h0v6juGKpPZtY1r4YK6GSrQJBgeCFcPzzo73oAg6RRYdAroWfq+/6un2nxsMVtidpWur4Xrk+MpkU/ljPXI1yfa+K6HtLy/XBzvZixH0akOiKvqXqVbU2VY+YNKZauqIq3gtwxkFHyIjlAteJjisnN0mIHRgmuLu7AKMHSYgdTTR6qFR9FcrClSmaFcFIvAhTOyC66E/496k+nZkdCxdx73oNSn6g0h0QfxC67+WrBmpi5en4UTNYeiuo2IRkil+ufryfInMT5b+0Y7a64BW8iSDoDEIt++XndFuwVHTSsXtRvdN+LphoiCuhkq0CQYHghXD98ydT3wvVDS/C7pAYifZ9N1xcUaML1OSZ5rrvemSI3vs8P14srhmFEqiPyqbbIEQwOfaPkDkoVXZCpjpIH+9f6NTD3DtaXGvQfmXlWDzMth1EkeL6zkF+3lYby99W083T6CvM0+xSqjf6wIkDRvThlGMC2hgfh3yOADtUe6l37V/eZBGL0rrVL7rjri5WrZ0Bl6f3/zT7XH3nngY/wu4xMkJtp14qj8vUEmRM3/+3WVZgK3BxcNx2TzRu6CIjXUzbhDEAc+ufnHXf2R0y/7F0kTdZ75PZrryOK5QmE64cvmfpeuH5oCX6X9jWcQL1qyonrV60K8PCmy4Xrc0jiXHc3PpeJ93/6+Yib/mz5Pl9cL27sB4lsfGkT9Zb1OkvxuwsBIlrkmIuaQ68z2dsHZT8F0bzcXcg5dicVYRdmqfSxDQ+dsyj6iLHc+/5IEnQHDPxfewmvdBcwx+KOCNtLh+jCPD18b0xLVGGeYDGgX7eVZrytfCbe92jvXonHnRfzunc+hWEhdqn0rg2iabDVPylUbOdQoAIjPibP28y256aDZmTp/fdz1U+7aPBN4kfd17MvUM1xSvOgHPtIZseaRXQeqozqRRvw6qFvVbP2hn74+he8+eSVBRit+vc2lYuB/jiPlvLOg9cgyxq2Cgdlkw4hSTBm9ieUjD+K12mmp7UYa2mXKJY3Ahgurs/WvgqiycT3Q+d6F98bc3TUuR5if5fee/Zy9jmljPrUB4npesnH2WdbuO8fuutnXvMV/vsfxcL1WSae690OO50HKygef4zTbluVE9/ni+vFjf0goKky65e/wPyvXYokpxPCmTr737qZgx9ch2J1hIpyBFvk+F0l1J2yMmkv28HYz9FGX369xqzetjKdAf2LPNfqokj206km/hq+3F3IXIubxYU9Uc9tdZtpcJupVnwDyt8Pr9Avo/GlMgf/0ViLOyz3qEgO8Dmri+NMPm4odYzIirrxvkeLFplp885ntrKHA4FqOsJC7FLNj9sX0IvtfKfgCeaa9vCRZxo/6L6J5j312Cpe4bRlpzNj4XiMP5sWCvczSem0QxIkIlb+m9dpxVrajdHqRer9qAcvAhqePpemD2dw/OVvJq12m6j3rOpXOO6sTVHrKJ/chGLxoanQ/mkNu7ZPjKq0KxheDBfXD9a+jjYG6vvBdj3AJreFI34jTb6+/RoNrofY36XPPp7JIZ9+w+XFhEfT0xYG4vpgXv7cuWbGfn5fKBrH+PLlwvU5IJbrHQeqkABJOpYz3+eL68WN/SAwGOFy4S1yfM4SILUWOYO9n6ONYHhdjyphCo6+A6BRZtD4z7J2ftQS/GLHG70Lhs5J2GUNA+AHelSZk209nGHvoUuVUXrz4IL5fIWyyvOdhSxvK0u6n+FFgIKFe44z+9jitoQuVpaWdHB+gRNZgmkmfTbpRIubX9QcpiCNbeUz8b5HSqkaEvr/di2Nan2TSn5cneFoRLEdk+xHQsXZWRiaoQsv3mOX44f6CTJBz38LeA3IxgBoYCnuzUnu97ULD5NLNAMQvBhIRqx1GK0+3A4bu/56emgdolje8Ga4uH6w9nW0kbrv88P1oPt+Qm8x3n1e46hxPcT+Lrm7C7FJHdxiX8Vm39SsuH6uaTfLCv7ADz/9MramOZQet064Pqf05bprqoS5qIeqmXvxuXsjUnLo+3xwvSielyO8zlJ6WsfT0zo+Ilwu+JjXWZp8JWmSSYucodjP0cTytjJ+0lKGV5NCIY+FBg2PZuBnraU83FIB9LW3kWMUqQmGzj3eVopblfhdWzEeVeL3bcUENIkn24sIaBKFhr58voAm8XBLecryDRbu6T8bUK34uKmsnSfqDnGKzYWnd/eMvb8cBQYNs6Slta18J9b3aNEilfMs6yNC7MJb38SqptsfhQA+lIhWOAEUPljzr/r3rWUij37xFwQS9jYWZEow/+2j3/wr2/90Fj63MTRqL0l6+GRPmx1Pt5myqY2EF4xqWjeD1t2ROXbp5sjFXsf4iHWIYnnDj+Hi+qHa19FEKr4fatdDfN8HXX+qrWdUuB5if5eCfeozdb1RCmCVPHSo9pDv/+E+Db9f0r9vLRNZ9+jLtAdGdxeCXBF0/eYVF/D6925i/9uzQNKL1kHufT/UrhdXEDkiW+Fy6ZBOi5yh3M/RhIzGZUVdbHZZIsLrNrks/LWrKPR3eH/7/nlswdA5nybxhtNOjybz164ivJrEG84CvJrEOKM/av3ptK4JzjTEmw3Y5DJznMmPNcYA47tO24hqkxPre/T226Us1eKH2CXLjwtoErd23MoJyl5e8pwR0Qrns49nEtj8HL6eUkBie2E9s4yfDtLRjh6CvWj9bjNjZnyG0RIZ+qgGJAyKitHqRVN9/cLk1NAMgKu1CGt5ZwY5cloW1iHIN4aL64dqX0cTqfg+UX78YLge4vvej4QEmCQNa4xpv5Hmeoj+Lvm76mno6utTn67rIbHvHfsW8NGvnsfvLma58QruLloxGIc5qgh3PYCmGqI0G9/3+vdhYK4eWteLG/scka1wuXQItsiZ8YWvUz75XVr3nMG21Y/hPDKdgrE782Y/RxPBG/ZKJRBVhbZI9vfm40kJc+bCQ+eC/x/sX6v/nU6V29ik0t/2ropjERcUjt62MOluK9+J9T3a9/yT7KM6ZohdKvlx2/z17A9U82X7C9Qrh3iy58JQnr67uxCj3U1B1U66D8+Mqr5bLDtzeryjhfBetDvWnEXtyTswWj2hWXuDouFTJRqeXsyRLZP7wuQklYV3PYli9rLx8Yto3T2B8mn70s6R618xN5N1CPKP4eL6odrX0UQ8359odVGteMkH10Ni3z/UUs48i2tUuB6iv0sTzEt55tv3xO1Tn0oufCLf+90lSAY3IBHAQHOgHBCuzyb9+86n7Pve3vZb/rh4QK4eatdLTqdz5DeozAKdnZ1UV1dz5nfHoVhSz2DY9uxPOfLx5aG/q+b8mRlX3ZaLXUT1m1D9pohetn53IbLiSdrLdjD3c6TTV0AHvlbWzul2F34N/vdYGds8FiabvNxe0coHPVYWF/Tg6B01LzRodAUk/Ehp5czVG738pPpwvyq3x7iteWxEldtU6H/z/s8uOz9qqQA0nh7XhEVSscsaLhV6NAMPHSvje2NaMtpWvhLre/THL+3ggxO+h1EKJFgykvD2eL91XsY630xOMTbwJfsL/NZ5Ket8s0KvlQxuDEY3fncJdqkHBX9K1XcFmVFYc4xTbvkTjsZKKqcdDD1+pGEiG357WcRryyY3cvJ/rmHD7y7h2PbjQo8rFg+q35ByOJ2s+JGVQGgWIZN19KdH9XBV+3Kam5spKipKvoAgKSPd9YO9ryOdVHx/V2ULJokEri9geVt50m1l0/UQz/flo8b1EP1dWnzGCUy+fA4myZc738seNNUsXD9IpOr7oOvX//pSOvaPzdjVuXA9pO57MWOfQzINl8uURC1y8mk/RzqxwtxcKny13BG6YV/aWMd0s4ezC3qQISJnrsUvccwv8+u21PId+1e53eC2srSxDm/audrxZwOCuX8/PFrBNo+F480evjfmGO0BJcNt5S+xvkfFxaQleYDVrsX81b0wIgyvwT+J2xy30EkhJrwcZzjEZ6YxeN1m/L1hmelU3xVkhh6qdwML7/pDWLhcB0V1xwANo90d1fJm3GkNOI/q38lkLe5i0X8WAYgQv2D4MlxcPxT7OtJJxfc/OFrJfVXHYrhe5ictZVxW1IWMlrRPfPZcD/F8X2/0jhrXQ+zvUibF7FLxfc1EhX37QFPTr7QvyJxEvjfaezBa9fc/6PoJCz9mZ2sxpgJXyPXpuHqoXS9u7HNIpuFyg81w2c/hQuwwN2jxS/yytZQPXVaKDAEuLepCkcCjShCWu77Pa2SO1ZNy39i+8L2+EDmnln5dzNj9bY9Rb/QlvKDwjaCwvGxyk/0FJhqaI8LwnJqNIrow4+O7hb9jrmkPf/qOjSfu/D6a1vchSLX6riBIeuGhql/BPqY9brhc3YJtA2pxJxhdDCeHDqd9HQ7E832rH/6vrYR3emx8raw9pus3uSwc9htT7hMfWdFe/83LxPUQ3/cKCNdnQCq+X7bjBqpnbMCx94zQcsL1mZA93x9/+VvUzNszolwvQvFTJJPwvIGGy8Ui0ah6piPuudhPQewwtw5VjhjdLzRoqL3fQAnw9v63WYZ3nVZ+1Ttrn6xvrIzGYzXNfP1QddJR/3gY0TBKWkjoAHZJFUIH/nb7Dl6vuzujZfu3xzvLuIGbC54N5e5t/tMufvCv/4fqNzF24jE6d9ZhxMeK0vvEDFoq9ObFvffQ1ZDGRW6ycLnaBduYdvFaLMV9eY9uh51dfz0trWr4uUSE4meffHE9CN8PJ/r7fr9HodbkT+j6blVmp8fE6Xa38H2esPiME5hzxbSMl0/k+7MbH6B0bDsBnwVr2QGkQ5MxST5+X3wfsuhRlho58H31vF1573pI3ffio5RDZMUbIU/Qw+UyvqlXZdb/8kU0NfptS/TcYO+nAMLD3Ha4TTgCMnOsbpa3lfKTlrKIljWyBD4NVHTZB/vfBqvSP1F3iKUlHQm3Fl5VP1N8SBGSB3COMMmn0qom29vr3zJnW6Aeq9T3Ph3eOxHVZ2PG57/Jfy6/jdvtf6BdK2Kvv2Zwd3aYUjapicLqVsomHUprOdWvRIXHhbegyUaLO8HoIBcOFb4fTkT73mrQYrq+S5VY7zKjSFBqUDnRood9D73vNZyaLHw/wO0l8v3WrZIeLfP5W5j/tcu4o+BJelQzX+u4U7S5TZFc+H6kuV7c2A8jHPtOxnlkGo7989N6TjD4hOek39xczQNHK7BKKvVGHy93F7LZFfkD87bTzsMtZXg0Q0T/20Q9ahP1nq9WfBTJ6eWE9ydRv93hyEAuhlNaf4zTFGyPd0fBk/yk5BHuKHgSl2ZmX6A69JrKcQc5bdnplE9+FwCT7EdCo1Oz52Q/RwJGuwtbhQNbhSOUFzdx0cbQY0a7Kwtb6WtZ49hXhafLGtayRiDILcL3w4d4vt/jMUe5/sMeG3cdGcvDLWW0B2TsvRPzQ+n7keZ6yK3v4w0YJPP91KlahOvnmnZzR8EfOKxWsN1fn/X9HCnk3vcjy/V5e2O/bds2Fi5cSG1tLXfffTdaCkNv77zzDnPnzmX8+PE8+uijKT+Xz3idpfS0jqendTyNa29EU000vncjPa3j6Tg4i46Ds2I+19M6Hq8zteJrQQZ7dHMkE8xJ3+jWC2wF89QafUbizea/3F0QdRGQqEft0pIOnqg7xM9rDjM7g1H/ZGRjViCfyOXFcECT+GbHbVGj7sH2eHNNe4C+ljl1hqOh1ygmP2rASE/reFqbxvK8axEBFP7qXkhzoJzmQDkdqrjJD2fyknWcec9KTv3WakonNwF9eXFn3rOSyUvWDXgbwZY1m1dcwNqfXM3mleejmPV+t4LsInyvM1i+F67PLvF9r8R0PWi83F3IJldkTv1Q+X6kuR5y5/t4rofkvjebQbF0h77nzYFy/uE5DT8Kz7vOEr6PQ659P9Jcn5fF8zweD1dddRWLFy9mxYoVLFu2jCeffJLrrrsu7jLHjh3j3//937nlllu46qqruP7665kzZw5nnXVWwufynf1v3czBD65DsTrQAkYAHPsWsOE3z+HrKQdNw2hvi3rO7yqh7pSVTLnw/pS2o6ky65e/wPyvXYrU27pFkDmRRW50gkVuglVno4vUedPqUZtK7/l0CW/dk6jfrk7+97P1OkvxuwsBIi6GzUXNgB6CarK3D3g7wb612/31zDJ+GnrcKAWiqunHqrgb/J43FHWDXx/cafBP4vaOW0QrnBjsWLOIzkOVTLt4LWabPlpvsnlwO+w0PH1uVkLo9Eq6N4bC91p3TeSNe2/s628/6OT/9y0ThO/7GAzfC9dnn3i+j+96H3t9xiH1/UhzPQyO7+O5HtL3/Q7NFVFBX/g+Nrn2ff65HgbyncvLGftXXnmFzs5OfvSjH1FfX8+9997LypUrEy6zevVqxo4dy3e+8x0mT57MXXfdxYoVK5I+l+9MufB+pl16N5Ks4neXAOB3lyDJAaZfdgfTL/9OzOemXfqdlG/qQYT2DSbxRvcViBu+H49YYf2JRv2TkeqswHAJ3dv/1s2se+Q1NvzmORz7Tgb6LobXPfIa+9+6OeN1d6j20Aj7865FAxp1D37PZYOKM6yirgGNb9hXC8nHIH5e3Am9fw3ss5ksB39QkVROv+OPII28GzHh+z4Gw/fC9YNHosi9ROl68cim70ea6yF3vs+m66Hve25AE75PkcR58CPI9TBg3+fljX1DQwPz58/HZtM/8LNmzWLnzsRtWBoaGjjrrLOQehOU582bx8cff5z0ueFAzbxnKa1fG/FYaf1aauY9l/C5ZCQK+8sklF+QGrGL1Ml85jMlCN+PR+yw/uQ/dLGfX95WFlXwJ1bu33AJ3Ut0oZzu4Fd/VrsW8xXHXdzecQtbe/PjgqPuX3HcxWrX4rTWVzPvWepPivxdGn2tcNIRdOy8uNPveApk34i6Ec60YNBwQPg+klz4Xrh+aIjneh9SknS9eGTi+9Hhesid77PtetC/57ONeyIeE75P/NoI33dbqJ67G2TviHI9DNz3eXlj39nZycSJE0N/S5KEwWCgvT1+CE1XVxcTJkwI/V1UVMShQ4eSPhcPj8dDZ2dn6F9XV1eGRzNwNA0cny3AaGulqG4TRlsrjs9OQdMSP5dsnbmczRT0J/kPWKKLgHhkMuofOQIfvV/xZgU+6LHmtFhfrhjI4FcibrK/wM32Z7I26q5p8NnHsyIq6jb4J42efNg0R6lj5cUZrR4Kq1s4+/srKKxuGcCN8NCf9MEpEDj0DLXv88n1kH3fC9cPNqn9dgyG70eb6yE3vs+26yF2BX3h+/j09/3ef34OyaByzn2/H/auh+z6Pi9z7BVFiSqeYzabcblclJbGHllWFAWzue8HymKx0NPTk/S5eDz00EM88MADmR5CVnEema63yPjC1ymf/C6te85g2+rHcB6ZDhD3uYKxsWc9wnPs7FU72fvqMnzOEkAf3TQVHmbapd8Z8I2PQCcbPWfjERz1D14gBEf9vQlapwRH4Gdb3HylrD3Gfmkxc/+Wlji4rKg71JcX+kL3CmWV5zsLY1bzHWrCL4atZQdwtY0PXQwPtE/8EsuHfOybEtG3NtNRd+eR6XhdFu4peJK5pj1s9E7lR93Xsy9QzXFK88B2dBgQPkrd9kkdyXLMgnlxkkHFVuHA32Oh48AYyqc2YSnpRpJg0pJ1+D0KfpcFn8uMz2lNviMZ9snNNpOXrGPCmVvwOc1IvTm1wYJBRruH/W/PZsNzpw7Z/mWLofZ9Prkesut74frBJZeuh/R9P9pcD7nzfTZdD/r33KWZ+U7BE8L3n9SSLJ/cebSUdx68BlnWKB53mMrj9yNJYC50DXvXQ3Z9n5c39qWlpWzfvj3ise7ubozG+CFKpaWltLT0VTDs6urCZDIlfS4ey5Yt4xvf+EbEMlOnTk3rOFIhlR8bW8VeTlt2eqj3bPmUdzlt2RnIih4alei5WITn2NXMe5b2vadx5OPLQ89nYzZT0Ed4KNsWtyX5AmmQqEhfOLEK5fy/kg6OM/k4y+5kg8tKpyoDUmhWoH/Bn5e7CtnrNWW1WN9gkOhCOd7gV6qEj7pXy600qxWhUfd0LyJsFXv59ur/x9zrxgN9FXVNUvzoi+GO0e7CaNV/q8JHqd0OG/O+9Fc++MUV+Lpj5y+qfgXVr3D85W8y4cyPUf0SBpP+uQye+4ppjZTVP4tkUNn/9hx2rFmUdJ/KJh+kqO4YZZMO0vbJ+KwcZyakVjAo/8NjkzHUvh8s18Pg+164fnDJpeshNd+PZtdD7nyfTdeD/j1/ouQ+bLL+3R3Nvp9x5ets+N3F+FzWuDfkql/huLM2jUjXQ3Z9n5c39vPmzYsodrN//348Hg9lZfFHCOfOncuzzz4b+nvLli3U1NQkfS4eZrM5YtQ/F6RanVZWvMiKN+IxxdIV8Xy854LEqxZqKmym/dPTUaxt2Mr3Z3U2czSTXsXZ3LO0pIPLirroDhuBn272YJTg7spWXum2Mc3sY1nzmISzAnt9JuZa3Cwu7JsBG0ixvsEg2YXyQAj2rc3GqLuseLH0C7eKVVF3JBFvlPq021djtHmYedUbbPr9xQnXsWPNIgrGtlIxvTHqN0uSQDYGaNk5LqHowy84pl70PpIEUy/6gC1PFQGkPgOQZZrWzaBi6gFq5+8KPdZXMGhkMNS+HwzXw+D5Xrh+cBGuzy9y5ftsuh7073Hwpj7IaPR9+dRGFLOPM+95MukN+Uh2PWTP93mZY3/GGWfQ0dHBU089BcDDDz/M2WefjcFgoLOzE58vekTroosu4v333+ett97C7/fzyCOPsHjx4qTPDSWDWZ02bo7dr5/H211JycQP+NxXL+eEq75JwGsPhf0JMiPXPebTJVgoxyxpFPQWyjH2fvs9GpxuczHR6OMb5W1Jcv9SLd6TH3lLoAs0KPkgiqUr6gI5E1LpUy+Iz441i2hYdS6aJmOy6bI12TwYTH4kiVCYfbIcs/XLr8Dx2diYz7XvHcv65Vck3I+pF63lzHtWcNptqygZfwSA4gmHOe22VZx5zwqmXrQ24fK5I3aBwHz6fg0U4fvsIlw/uAjX59dvUa58L1w/cEK+Rwr53mjxIUnQeaic/e+cOIpdD9nyfV7O2CuKwi9+8QtuuOEG7rnnHlRV5R//+AcACxYs4MEHH+SSSy6JWKaiooL/+Z//4dJLL6W4uBibzcYvf/nLpM8NNqn22cz2KPqUC++PmWNnLDjMlIvvYdwpfwSyO5s5mslFj/mB8nJ3YdQIPIBVBo+qIUmgSFCt6BfSsWYa4oXu6X159XDXXOcZ5hOp9q0VxCfWKDWSLrL+OWbxR+I1LKXdBHwyBmPfbGjAJ2Mt6yZZvr7qM4CkYbS5kXqvcw2KimxzowWfHwKCBYM2Pn4hrbsnUD5tH3Nv+DuFNS10Haockn3KNqPd90Zbu3D9MEa4XrhekDpN62ZQf+5HWIoiP5u28q5R7XrInu/z8sYe4JJLLmHLli1s3LiRBQsWUFmpH9SOHTviLnPTTTdx7rnnsmvXLs444wyKiopSem4w2f/WzRz84DoUqwMtoOcQBqvT+l0l1J2ykskX/DClkL10iZVjVzZpbUj0QWKF8gvSJ5ZchzaUrW8EXgKKDX2freCYfbICOakU78l1nqFgpNE3Su33GLGVdWLovUCOzjGLjS5ELwGvEdngwe0owFLSTcBnRDF7k4rxk1cW4Om0M+XCDzCEVXxW/Qb2vHQKjetmZu9w0yBYIDDYY7d110TeuPdGVP/QXXzkgtHq+9oFT+LYN1+4fpgjXC9cL0gVDcXsw+82IisBZOH6ENnyfd7e2APU1NQkzYXvz6RJk5g0aVLazw0W8UbTw6vTtu89JRSyV3rcuqxtO5fVwQWxiF1xNtmIYq7oG4Gv4I7KNroDYOu1fDBUL9lMQ7ziPUVygIo8yjMUDCYD+zz3H6We//U/UTntYOj5VHLMnEdLWf+ry5j/1RfY8LuLObp1MmNm7WHO0n+y/leX4TyauE+3nvv3MZIcGfImGwNMvWQt5mJnSsV4sk2wQGA4QemPNEaj762lTTStu1a4ftgjXC9cP1rIju83/O5i6hZsTzuffKS6HrLn+7zMsR/pxOqzWTz+I0omrqendXxEyF5P63h6WsfjdSb+sKZCsFroCf/+TZFjNwhk0mM+lwRH4B0BBauk8oOjY7hs/zg+dkVWjM5kpiHf8gwFg0SavWhjERylbt09AdAoGOPA02XBsW9MjByz2Llmql+h8+AY3rj3Ro5unQzA0YYpvHHvF+k8OCbpiPeONYvY84+TYz635+8nD5noBcOfWL4vrP2YkonrhetHCML1wvWjgqz6fnxfPvn+Sjxdliy5Xom7LIwO14sb+yEgfDS9qG4TRlsrrbsXxS5485vnWPfIa+x/6+YBbzdYLbR88rtAX46drWLvgNctiCYo141uvcJmMJSt0Re/jVMuCRbKCd8vpyZRawrQEZDZmbBATmKCBXsCmkRhb8GeQoNGQJN4uKU8b3veCgZGeC/aTFH9SmhUOjiav3nFv7D2J9eweeX5KGYfhTUtSS8qwtcTxO82owbklC5Gjm0/Di0QOROhBSSO7Tgu42MTCPr7XlJctO5cIlw/ghCuF64fDWTT932uv4C1Dy9l88oLBu56v5LS4MNId31eh+KPVOL12Ry38FGa1l8bN0R/oCRroyPILqn2mB9swvcrlQI5qZJ/eYaCXBC/97z+Pg+kXUyiHLPwi4q2T+pSXmeqy3U3l+PpsiErKq7WIqzlnah+me7m8oyORSCA2L7f8tRvQJPxu0sA4frhjnC9cP1IJVe+z4XrITXfj3TXixv7ISBRn82e1vqIgjel9WuzInqBIBapFMhJnfzKMxTkhni951OraJuY/jlmmV5URC63MaXl7GMcKGZ/VEVa+xjHiKlALxh8Yvl+4XcWsPOF/+Zow6Wh1wnXC3KJcL0gE3Ll+2y5PnJZLaVlR7rrxY39EBBvNF0UvBEMNtmcacjmjIAgf9mxZhGdhyqZdvFazDa932yqFW3TJdOLir7lTBjtnpSWGy0V6AWDSyzfG8xddOyfL1wvGDSE6wWZMFi+H8gAQrAgHoDfY0y67Eh3/dDHCglCiII3guFMvuUZCnJH07oZtO6ODHPrai7P6k096BcVDavORdNkTDb9Bt1k86CpMg1PnxtX9MHlJBnk3jzQZMslzNsTCLKIcL1gOCNcP7qI5ftUKtinQ6auDy679/W5SLKGyeZNuuxId/3IOIoRQqIQfYEg38nXPENB9jHaeyif2ojXqcvRZPdQNqkJW0U7IKWQd5d6yGbTuhlUTD2QYlscDaPdjdHqof3TWgK+yBF4x/4qjm6rT2m7me6vQJAM4XrBcEa4fvSgh7m7KZ/aiOqXkRUV1S9TPu0Atop2fC5LCjn2qfkzU9cDFFa1R0U7te+tznDwYXj7XnwT8whZ8YZEH0SxdEWF8QkEg0d6FXMFo4PjL38Lc1EPoIb8JxsDnHbbKs68ZyWTlyToyZ12yxytry3OvqoYLfD01wTXO3nJOs68ZyWnfms15mInAGpAQlOhavbefvuWwuc7Cy1+BIJwhOsF+YdwvSCaoE/NRT0E/PotY8AvYy7qSe56SNOfqbgekAJRri+d3KSvQe31vQYVJ+wn6johGSPA9+LGXiAQhNH3wyejsbymGVkIX9CPravOY+szZ6P6TaGwOUkC1W9IGjaXbsuciLY4P7k6sgUehERcNvkghdWtHGmYTMOqc0HSkHsNJxs0fD0WAl4DB9fNiFgumcCz0eJHIBAI8gvhekFydqxZxNZnzsHTacNo8QNgtPjxdNjYuvqcpIXz0vFnUtcDSCpn3rMywvXh4fuSDN4uG3tfPwlUOeo6YTT4XtzYCwTDmmyIWF9Hf7nPsng4zuRjpkWEhwoiUf0Kje/NiZF3N7439C3yc2m0u7BVOLBVOCKq1gYfM9pdcbcVLHTTunuCvo3eQjfOo6UY7S6q5+6isLqFqRe9H1pv+6e1OPZVR6zn2PaJvP69m3AeLQUSC3wg+ysQCATZR7heMDQcfH8WrbvHRTzWuns8Bz+Y1ftXdnyfzPW2CgfVc3dhr+iIdP3+qn77No5dL5wVWhZGl+9Fjr1AMEyR0XisppmvH6pGzTAfKHwdQbmfZXey02PmiqJOjBJcUdTJsd5qoU5VplMdGZVDBQOlL2wu2Au2bMpBPUxu2Sree+hq6M27HEjF2/5tcQAkg4qlwMWkJeuoW7ADSYLi8UcAKJ9ykFNvfRpTgQdNhY4DVaF9kwwBLAW6pBO1xcllSz+BQCBIB+F6wdASx/VoIGmcvuzprPg+luv9bjNGu4vpl75N7fydaKqE1DslXT7lIKfd9jRGe7TrQQtdJ8Do8r24sRcIhinho+xb3JaM1rHA2kN9r+DPtvdglODuyla6VAlD7yjsHIubn9ccplBWeb6zkOVtZdk8DMEwJRg2178XbN2CHaGR8bZP9Bn9gbXMiS5kEy7igNeAYg5g6BWy0erF7zWg+g1IcoCdf1mIZPAz94a/c/zlb1Ezb0+vwANAbIEPZks/gUAgSIRwvWAoief6wpoWjDb3oPi+dv4uAh4FxeILPW60etE00FQJ0CJcX1jTQt2CbaG2t5Kif8ZHg+9FKL5AMIwokgNUKz6qFV/EKHvwsSI5kNY6/rO8HUWCb1e0Msei/6B5NCiQNQp6B+sLDRoBDY75ZX7dVprLwxMMI8LD5ox2F67WEj549AqqZn8SM5Qto5Y5cfLiwlvjKOboz3zLrvG07hqHbICJizbq+/bzK9j5l4W9y0lJ2+IMRosfgUAgiIVwvSBf6B8i33mwig8evQI1IMcNXc+F7wM+U2i2PkhnYwUtOyZEud7TbdWXW30ORrs3aQu9keR7MWMvEAwjlpZ0cFlRF12qjJLyKHvkCGisdSgSmHp/MK0yuFUwhA2a7vMamWP1DGjGQDCyCA+bSy2U7az44Xyhz2fkZzU8Ly44GxAkVmucgE9vx1M14zN8blPc/aiZtxNLcU9oudgCTxB+OIxb4QgEgvxHuF6QL/QPkc+O76M9mtz3+6mdvzv0mKZBUV0LPndn3H1wtRQj9RsoGOm+FzP2AsEwYnlbGT9pKSOgSRQadFHro+wSD7eUR4k+VrXbWOuQ+/1uaUBHQOITj5HOgMQsiyejGQPB6CB8Bj3eyHiq1e2NBc6UCtkY7T2UT23E22NE6/14a5pE4/vHg0TUjPz2Py9k/7uzWXjXk5TW661xvE4znm4z5dMOYLT3RBxTShV6BQKBIAcI1wvylQH7PmxmPtXCdUZ7DxXTD6Bp4HPpISaqSlLXH7doA5KsD/p3HizH022mbGoj/Qv+jSTfixl7gWCY8XJ3IXMtbhYX9t2IbHJZeKW7IOq18XLzYq3Do8Jer4k6ow+7rLG+x8Rpdg8uFUy9FwMiB08Qj1gj6uEj48FwPr/brD/XW/FW7S3WFBytn3nVG1TN3pu0kM3xl7+FuagHn8uEz2XCZPNiUAKMnbM3apC9dfc47BUdnHD3k0iy1ud0CSRJw2Tv4fjL32LLH/4ltEyy/RUIBIJcIlwvyFea1p0QFTGXqu/DZ+arZn2SUuG64y9/C1OBG5/LhC73ALIEql/CYNQi9iHc9dayDgDUgAFzUY+el6/qN+xdhypDy40k34sZe4Fg2KEx2+rBEZDZ4TbhCMjMsboJ3q2klpvXt469HiOqBi5N5ubmsTxwtIIeVeJJhz7a79EMSL2yTzRjIBjlSAGqT/oET5cFx74qPF3WsFA2PZwvKM3QIgYVS0l3xGi9ZFDZ9ddTe0fi4+fFbV11HtueXYTqM4ZG7CUZVL+Mz2XG02XV96PbQvm0A+x/Zw5dh8qRJEJ5eiabB9WnsO2Zs9m66rzevVLj7q/fbY6q2isQCAS5QbhekIf0zriXTT7Y59kkvjfaXZgKXFhKuiNm5ve/cyK7/noqGlIavu/rWS8bNPxuY1zXB2vwGC0+NE2i4enFvHbPV3rb4PWF6I8k3w+/PR6FaBqhH1uBoN7owyqp3He0ko1uK/MsLr435hj1Rh97faaUcvNe7ioIraPBbWG+1cm3K9uoN/r40G3jmsY6vJrEXp8p5RkDweimbsEOJIPKp/+cz74350VUzg0fGQ8nXq5e6cRmTP16x/bPi1P9CgfePZHS45ojZg06DlRRNqmZTSsuoHX3BE668UXGzvmU025fFTNVzu8yc+C9Ofofsp/zH1zOy9/+GqhCj4LBR/heEES4XpCPlE1qorCmFdVnYMPvLo6qlB/L98ny8nuOFUNR/Lo38XyvqXJoH1JxfWidsp/zH3psRLpezNjnOZoqs/6XL6Kp4q0S6DT6jFzTWMdGtxWADW4rSxvraPQZgdRy88LXEQD+o7ST/2isDa3Dqcn4egucJJoxyA7ZXJdgMAnPj6ua/QmSpMvaVuHA1VrCuw9e3TsyHptEuXq+HnPc2YA++greBF9XVHeMN+69IVTBd9Pjl+gj/X5DaBsQnNk3YjD7QuuduHALsjHAhIUN2TxNAkFKCN8Lwhl5ricH6xMMBlG58AaV1k9qcbUVY6tw0HmwijfuvTGu7xPn5Z+DwezLwPcWPF1WWnePB4Trgwh75DmOfSfjPDINx/75Q70rgjzBh0SPFvnV7ZOzzsvdhWx2RYYVhY++h68jmJt3nNkXsQ7omzH44dEKbm6u5oGjFVgllXqjL+xVmYs6VsEfwfBh8pJ1nHnPSk791mpK65uBvlH4M+9ZycSzNicNZYvVZqbjYCWSTJxCNn2fldgFb/xYy7oi1nfg3RNp3T0u4rHmjVPZ+PhFKFYPdadspWzKAeoXf4QkwaTF6ymbcoCyKQewVbYN4AwJBKkjfC8IZyS5HoTvhzMRrp+sF58trW8OuX7yknVJQ9fjtZTrPFiVoHBdIt9fgGL2RxS4E64Xofh5iddZit9dCEDj2hvRVBON792IuUi/cFYsXZjs7UO5i4K8p2/0vdmnUG30h42+SxTJAeyyHg4Vnpt3rLdQiFOV6VQNodH+4IVBcMbAq+kXBTIaj9U08/VD1agZtASJV/BHMDzYsWYRnYcqmXbxWsw2PXTeZPPgdthxO+zseP7MFNYS3WamqFafdff3fiZChWwCEqff8Ufee+hq0OR+BW+0BAVverfRbcHVVoS1tIuyKQf5+A/n03OskFlXvxZxzWou7GHB1/8Mkt4n972Hrs3K+RII+iN8LxgYw8P1IHw/nEnk+oanz2HiWR/r/ee1RPPFsVvKNaw6N3bhuri+N0W+LsL3wvXixj4P2f/WzRz84DoUqwMtoIdLOfYtYMNvnsPvKqHulJVMufD+Id5LQT6Tjdy85W1l+JDwaZESd2oywV/G9ESd3oWGYHgQq598V3MZ5VOaYvaj7U9wFH7j4xdG5OpZy7roOhT8TGn43WbKJjdG9LlV/QZ9hkBSOX3Z07z30NVRBXASbaOwpoX3/vc6Zl79CuMWbA8V1ZNk0FS9dd7WVUuydaoEgiiE7wUDIfeuB9Aycj0gfD+CiOX61t3jcLUVx+0/H048D9vHOMLy8vXPTrTva1H9CmpAjrjZ7+974XpxY5+XTLnwfuxVO9n76jJ8zhIA/O4STIWHmXbpd6iZ99zQ7qAg70k2+r68rYy9XiM3lnZQ2lvIpNCg0eKXebilPGHBnBLZz0PVR/mvIxUxRK3hVA1Rog4f7U/1QkMwXNBHyL3dFtyOAgp6RRyseut29IaEusz4nNaopZO1mTEWODnl5ufY8LuLI6rpuh025n3pr3zwiysoHNuW8MIi2Ta2Pr2E8imN2Cv6Qvh72gpHjOgF+YvwvWAg5NL1RXKAAjnAfVUtHPIZYtyUS3T2KzzWf2Zf+H4kETbj3l5AUW0L5dMOoFg8Kfk+aUs5SeWMbz/Fxv+7CJAjfD/jytfZ8LuLKRCuT4q4sc9TauY9S/ve0zjy8eWhx0rr1wrJC1Ii8ei7Tjo9csO5pbyNiUYfv6w5HHpMF3UzJbLGc50FLG8rj1gmfLR/IBcagvwjOELesruOqll7kWQNSdZbzMTqR9uf0Kx7GOGj8DOveoOC6jZOu311qNJt2eQmTl+2CsXqZc61L6P5lYQXFqpfSbgNULGWdqOpEPAqGEx+rKVd6O1wRCkaQW4RvhdkSi5dv7Skg8uLupCBKoOeax9+U96lyvz7gbqI0Pz+M/vC9yOH8Nnw8advoXjcMcxFPb2F7pL7Ppnrg/3tz7znSXxOS6h6fsXUAxjMfs767pP0tBYI1ychL49i27ZtLFy4kNraWu6++240LbVCG48//jj19fUUFxdzySWX0NzcHHruyiuvxG63h/5ddNFFudr9rKBp4PhsAUZbK0V1mzDaWnF8dgopngqBIAVSr4Kr98v1Uq34UCS9HZNJ0iIq8QZ/TN7vsYUtE6vHrpctbgvbevOkgojWOsMTT7eVDx69gl1/WdjXJ773Os9k8/T2jj035k19sB8ukhrxcHgFXtmgIklgMPkjqukazD4kCSqnH6B0SiMAZZMPRhTzSZWqOZ8gyRo7/3Iar3z7Znb+9VQkWaNqzicZnRNBagjX6wjfC3JLehXvi2Q/1YqP5zsL2es1Iklg651ULTRoqJpGZ0CmQFaZafEkdH214uP9HlvCAn+C4UHQ9a62YmSDhiSB6pMxWvVBn1j950Ok4PrjFm1CkqHrUDlIWsj3isWPJIGmaljLuwHh+kTk3Yy9x+PhqquuYvHixaxYsYJly5bx5JNPct111yVcbu3atdx33308/vjjTJs2jRtuuIG7776b3//+9wBs2rSJDz/8kNraWgCMRmPOj2UgOI9MJ+C1M+MLX6d88ru07jmDbasfw3lkOgVjdw717gmGNXoOU7LcvHCuLXHwb0XdOMLC6fqPCqqahCRp/L+SDn7corC0pIPzCpz9QvBcPFGnF17xasQt+CMYPkw696Oo/rThRPSO7UdwhL5/WF2snrdIkRehmqa3aEICU+jCwttbzOfcuNuMRcvOCbz+X1/E06EXMfvs1QU0f3Q8Pld0vr4gOwjX9yF8L8gdWlqul9H4v9pDFBm0CHeHo6pQblSRe2/gu1SZ8wqcdKsSwSS8cNc/31mQsMCfYHgQ0/X9LgT7958Pko7rreVdKGZv9A7IIMv651G4Pj55N2P/yiuv0NnZyY9+9CPq6+u59957WblyZdLl9uzZwyOPPMI555xDbW0t1157LZs2bQKgqakJTdOYMWMGJSUllJSUYLfbc30oA8JWsZfTlp1O+eR3ASif8i6nLTsDW8XeId4zwXAmvN1Msh654bzXYyeA/oMRnKU3yvpMk6aBqkGJQf9Rnm728FjNYc4rcNLgMvXrsQsG4JUuOwFNStJaRzAciNWfFmL3joUY/XB7w+r0x9ox2l0x12lQVDQVPN1mNFX/G/qiA4K0761OS/QAAY85JPogbkcRAc/IkX2+IVzfh/C9IBcEfd/kU1J2/SyLh0KDxrMdhRHuBgj0+r7CqCH3/u6eaHFzms2FRP8oPt31z3QU8XJXYQqt9AT5TiIvO48VRvWfz9T1JpsHSYKA14Dq7xO83O+O1bG/iqPb6tM6htHg+rybsW9oaGD+/PnYbHo476xZs9i5M/mI9fXXXx/x9549e6iv19/wjz76iEAgwJQpU3A4HFx44YX87Gc/o7S0NO76PB4PHk/fRWpXV1fc1+YCWfEiK5EjVoplcPdBMPLon/+WKDevfzVbRQKPKkFYXbyjfpkKRc+wC95gGWUo1FQ04FOfmU7Vz1kFrtAykqQPAtzWPBaXplfJ71/wRzC8iFUtt3njVA6unx6qSBusehtrhF7PzVuFqdDNvjfnsGPN2THX2bJ7HN3N5UxctDli+5qmV7aVZI2KE/YjZoLyH+H6PoTvBbkg6PtpFm9UJftErjdKMM7oZ5fHRIUS5m7ArYEl7Ke1wKChaRrrXWZcqhzl+vFGH34NbmseS0tAv3gQvh++xPPy+l9eEVGBvutQ5YBcr6kSH/3mX5lx5ZsUjI1s96kG9OjQqtl7cbWti53mN4oZshn7z3/+89TU1ET9W758ORMnTgy9TpIkDAYD7e2p93FtbW3l8ccf56abbgJ08Z900km88MILvPPOO+zfv59777034ToeeughqqurQ/+mTp2ayWEKBENO/Pw3/bGi3kJn/Vla0sETdYf4ec1hZlvcAFQoAdTeUXuPqn8/H24pw9svWs+rwUMt5SxvK2W+zYWmgb93Zh9glsXNg9VHeKLuEEtLOgD9QsMnbsaGKX29Yx0HxoRG7lt3TeCNe2/EebTvxiruCH1v25kjDVMi19llxbFvDJ4uCwVV7exYcyY+pz5rH0SSwNtlY+/rJ4EqU1jTElqHYGgRrhcIBo9MfB/L9XMsLk7pdXebX8YRkOlWJZyqHBUltc1j4q4jVTFdP8fi5ic1R1he2xxyPQjfD1/CvLy/Cm+37mXQQhXog77PzPVVeLoseLqstO4eh8Hsw+cyogbCZu4NGr4eCwGvgYPrZkTsm2AIZ+wfffRR3G531OOPPfYYUr9fDbPZjMvlSjjqHs6tt97KggULuOCCCwBYtmwZy5YtCz1///33c+211/LII4/EXceyZcv4xje+Efq7q6tLCF8wLMm03UysarayBB0BiV+1ldHqN/C9McfY4zFFLQsSr3TbqTf6CGgSz3YWcE5BDxWKflEhquKOLBL1ju3rT9tHrBH6gM+AqUALVbu1j2lDMXvZ+PhFEevUi99Ay546KqcdDC3fcbCSXS+cxacvn6K3tgnrbY+W7hi2mPHPFsL1AsHgkYnvY1eu12/OV3cU8tv2slBe/iceIxVKX4SLV4UxSoB6o1e4fhSQzPX9+8oP1PWK2cfG/7uYulO2Ufu53aF1HNs+kW1/OjuiVV5mvh95rh+yG/uqqqq4j2/fvj3ise7u7pQL4KxYsYJ3332X999/P+5riouLaWlpwePxYDbHzqswm81xnxMIhhMDaTcTq03Ohz220DJLG+uoVbyYJOgOQKPPxDijF5usUW/00ugzhXrslhkCabfbEQwPkvanjUIfofd7DBhMAdDAXOwEIlvmNK6dSevuCRHr1DR4494bWHjXH/R+uq1F2Co6KKo9BmihfSib3Jiw321cBjQgIOiPcL1AMHhk6vtYrn+z28pv2/VBAD18vpYn6g6hatDoM1Bs0NDQsEoqCgjXjwIG3/U34nebmL30n7rv2wuxlXVSNuUgfreJ4E15vOJ8CRmhrs+7I5k3bx7r168P/b1//348Hg9lZdEziv356KOPuPPOO1mxYkXExcTSpUv58MMPQ39v3LiRqqoqIXPBqOHl7sIM281Etsnxa0S0ydHz9CScKvzg6Bhubq7mvqNjcKr6qKEPiR5NjlpPsnY7gnjk5/lS/UrUSL3fbY7qJxskOOq/4bf/ypY/nofPZQ4VxglvmbPt2XOi1hnwmLGWdaGYfWxecQFrf3I1m1ZcgGL2UzqpKUGxHgdGuyvG3kQSfoEgyB3C9QJBbsjM99GOXljgQqYv56lKCWCUVP7rSAU3NtXxwNEKFOCew2P4zGcSrs86+XfOBtv1frc5tI7NKy5g7UPX9PreN2Dfj1TX513xvDPOOIOOjg6eeuopli5dysMPP8zZZ5+NwaCPBnV2dmK1WqNG9Y8cOcKVV17JbbfdxkknnUR3t97rsKCggJkzZ3LnnXfy4IMP0tLSwn333RfKyRMIRgcas63utNvNhLfJCSDx4NgjFMhqRJucz3wmrmkc1yv14Mj+uIjCOPHb7XjZ68u3i+6BhmblKLRrBI0u94366yPu/UP14rXMiV4+ctZgyoXvMWHh1hjFevRZgf1vz45ZaMdod2G06uGl4RcIbod+IexzmfE5rVk6egEI1wsEuUNNu71cf0dfVdTBV8ocnFfg5OVuvYq43kmnv+v1InjBfPnh5XoYmK9zGMY9Qnwf6WotbddHr2Ngvh8Nrs+7T4uiKPziF7/gm9/8JhMnTuT555/nvvvuCz2/YMEC/vGPf0Qt9+yzz3Ls2DF+8IMfUFVVFfoHeg7d9OnTueSSS/j2t7/Nl7/85Yg8PIFgpDPJ6KXcoPLA0fK02s04AjK3NVfR7FdClfE/dpnxaYQK8fTNyvfRvzBOrNZ61zbW8u3KFuSURqUHZ+Q6vB1gRkgqp9/xR5Ci+7kPlJE0uqz6FfweY++5CvQrnBPZMifu8jFmDXb8+ZyYxXqCswLxqudOXrKOM+9ZyanfWk3p5Cag7wLhzHtWMnnJuqwct6AP4XqBIPvIaDxe24xVCqTVXq7RZ+QrTdU0+xWqFR+zLHrLsdNtPaHCe1ZZHQTXw7DwfQ5dDyPH9yFXSyqn3/FU2q6PWEcYmfp+NLg+72bsAS655BK2bNnCxo0bWbBgAZWVfcWXduzYEXOZm2++mZtvvjnmc0ajkeXLl7N8+fKc7K9AkO+UGFRUwEf0SHsiPl/SGVWIZ6bFw09rjoQV4ikl2ai1Dymqtd4ks4+JJn+o9V48ZDQeq2nm64eqUXNc5KR/O8B0ySjPKwH66LIbkEbc6HLwXNWdvCOtwnvJiFWsJ9mswI41i+g8VMm0i9ditunheyabB7fDTsPT5yadURBkhnC9QJBdZlk81Bj9/Nfhyqi+9Yl870Pi34q7onw/2+Lh5zXNFMoaz3cWsLytPOH2B+J6yK3vX7rwfV4770EAHr51DbMsHqZY4Pr5Y/n9+iPc/tPL4aeprSvbrgcw2nswWvW2lyPV96pfYcPvLs6K6yF9348G1+fljT0QaokjEAgyI7w37aVhbW+O9RY5caoyviQhXvEL8Ui0+A38tq2E5TXNjP9CG6+P+17SfQoPgzr+srcxVsOdx+zsfP5MILa4yiY3Mvk/13D/iydnTaDp7tPrP707peWzLePJSz5g4qKP8XZZkBT9QiuV0PLUGdyKsLHO1ZjZn/LBo1cQ8Jow2ntSKMaTjL7WOa7WIqzlnWGzAvGPNZMBAcHAEa4XCAZGZB/6DowSXFTUTaNfT2NxqjKdauLf05999REap73JtgXbmHbxWiy9Bc4KDRqqX6bh6XM4b+EWbD+7NKXQ8ExcD7n1/Ws/fTC0X9978myOv+xtpOojLH69hbrnz4YKR0Jf5zSMW1I58+4nMdrc+JyWtFLJUmeofa9xbGctrrZibBXtdB6sGqDrIRPfj3TX5+2NvUAgGBh9bW8kiuTU29z1J1a13H1eE3Osbi4p6uY4kw+1ZW1K+zR5yTomnLklaU7UYOZBpbpPuVo+EUcaJjPhzC0gExFqlpXR5SHI4Yt5riYd4uT/XIPR7sbXY+G17345KuwuHdJtvddHZgMCAoFAkAteuvB9Lnzp1KSvG6jrX7rwfV6bps9kx7rpkWQVo9mX1gx1Ol4cLr7PpevLJjVhtHrY+/pc6k7emf3Z5DzxfWl9M6d+axWmQjf73pzDjjVnD2gbmfl+ZLte3NgLBCOU4Gz7V8ocGCRd9pn1lNWr3HYEJI75FaoUH7MsbowSXF2szw4Edv6IonFV+F2WhBJONQwqlwLNdJ9ytXx/oi5yDBqqL/KnOhujy7kIJUxGonO19/W51J+9acD7k347Hp3MBwQEAoEgu/zsq4/w2rQH+dvBHexxvsK3fvXNuK9N1fWLzzghatk5V0zjtZ8+GPaIRtmUg3i6zXg7bdgqHRiMGvWLP0JWVCYtWYffo2TN9TB8fJ9z1ysqBVXtOPaPYezsz0KvG2m+93Zb0FSJIw1TBryNTHw/0l0vbuzT5DHfQxTKtqHeDYEgIbKhA1nugS6wl9wFhtbQc1bX57im4xt8IVCc0rqM5s8ol+7C7ZzNpMIP0TQFSfIDUGbQJSwffYNTv6kgGdSkEk4lDGqw86AGGpqVzdCuWBc55uJuNBWQIOA1ZDy6nA8VYWOdK8eBMRRWtWdlf1S/EtV6J5UIgEwHBAQjE+F6wVCybdprALxep6eB3c7lzPj6uVGvS9X12x57LaX88cKaFkx2F5KsYTD6kXtTwMxFesRexbRGyur/lDXXw/Dyfa5dXza5CaPFg6aBp9OKJDPifB/wGTAVaFnZl0x8P9JdL27s02Tnb97EJudjyw6BoI/jL38zJAzZoItSDUhIkoatYB1HP/KkPAIuK35k5Tr8bjO1Cwp68+/0G3up1zOSBL4eM7v+eloKgtMom3wwaRjU4OZBDTQ0K0uhXZoW8yJHlsHbbWHH8wtxd9gzHl0ezJmR+PSdK2QVk81D1czP8PVKdvD3RyfTAQHByES4XpBvbHvstajHsul60G96XrvnK1TN+YRpF69FMeu59rl0PQwn32cvjHvHmrNiDmhoKux9bS67/nLmgGaT88n3fo8BgykAEKrfIFyfG/Ku3Z1AIBg4O9YsomHVuSBpyL3fctmg4euxEPAaOLgudVkGW40Y7S7aP63FsX9MzNe17a3ulXC81iW9IYK1RzEX9eBzKaz96efZvHIJ72VlggAAO+xJREFU5mInhbXHol4fvzVKdtvhBEOzNq+4gLU/uZrNK89HMfsorGlJsJQ2wOUjkdG44dhakFSa1s2gdXdkuNyx7RNp+nBGaHTZebQ03cMMfS7SbQeXGbHfo9JJTSgWLzufX4jj01r9glFiEPZHIBAIRhbZdD30+f7o1vq4rnccqOLotnqy53p9mdi+z347uYH4PhuuB933p9/xR5o+PD7K9c2bprDrL3qRwZHi+23PnEvnwQokCaTez6nJ5kHTJOH6LCNu7AWCEUrTuhm07Bof8dix7RN5/Xs3ZSQJvf/nE1TN+kwPCwc0re/fmBP2gxSI3ds1rOeruUAfmbaVdVM26RBaQAEVTL2PB4kr0NpjWe8fGwzNat2tn6+kMu3Xw7Zv+Qlxlk8+EDHL4qHS19XbtzbRoIY+utx/xDlVYg0axJ4ZGcDgSYIev2Pn7MZg8jH90ndCfWTVQORMx0iqUCsQCAS5JNuuB5i85P2Q67VeFWgaaCpUzdrL5CUfZM31EM/3Xhbe/WTWe8UPxPfZcD3ovtdz3puiXF9a3xyxntz7foATJSn63lreFfW8z2UWrs8y4sZeIBixaJRNOhR1c+h3mzKSxCevLODwx5OAvhFXSQI0aN5cj6ZJ1J28PVSgJZwxM/ZSWH2MCWdtpn7xBmSDhqxoTLnoPaZetBZZ0fOtbBUObBUOjHZXXIGaCnpibmMgqH4Fv8cYIadEMg0vRBNavl8oV2j5BNIz2l2U+J1UKz6uKOrEgH4eKk/4DMXiZevqswc0KxCbxIMGQJx9Tl3+/c9PODv+fA4NqxZHzCIYFA1NBeexotj7IxAIBII4ZNf1RrsLT6ee+6yp/cLw3Qqqz0DXoUoKq1uy4nqIPTi+eeUF2CscWXU9DMz3mboeAE8L1Yov5HtZUZl8wQehCLb1v74sy66HpL4foOshfd8DqH4Zv9uIYvalvT1BYkSOvUAwQslW5c9gAZZJS9ZRfdKnIckHUQMyY2d/xqGNkzn+8rdDBVoCHgWDxYvfbep9HE64/O2I3/Cy+ubQf1dMbYzKtwqKNrwIzMSFW3JSBCa6amxkzlymhWgSVaOdvGQd5xz5mC/USCj09akvnXgIg8lH2aRDHNkyNavFXVL5XETtcwqtctI5P7HyKVt2j2P9L68YcRVqBQKBIJfkwvV1C3YgSdE/90arn6aPplI1+5Pem/QNWXO96lciPDL+tK2hgYBsF3yLdFwt/fPjM/F9ssrzys4f80TdIbpUOeT7orpjqD6ZWde8GjoX2SzkluyzkYnr0z0/sXzfvHEqB9dPF67PAZLT6RRDJSnQ2dlJdXU1z5Z+TRTUEQwL9KJ3gYjRZcXiQfUb0hrFDy/OYzB7UcyBiOeDofh+lwmjzYskgbfHjGzwYzAFUH0KAb+hN5+KqIGBcNwOe8yiPMdf/gYTzmwIFYEx2Tx4e8xofnlAhVfC5XT8ZW9TNXsvh7fUs/P5M5j3pb/ywS+uwNdtjzoPyfYh/nr1vLlw6WkrX2VByw4qlL7zGu88pE78Qj7xPheyyYdi8sfc55IJzcxe+k8+/OW/xW2Vk875AY2zf/B/yEoAV2sh1vJOVL/CG9//IiBl9DkdjfSoHq5qX05zczNFRUVDvTsjAuF6wXBjMF0P4O2yIJsCGC0+fG4jkqTmveshvpftFQ42/O5ifC5ryMup+iwd159z8AEe/XUdN5Z2ZNH3iYv2xfpsWMscyMYAWsCQkevTOT/BfdR978fVVoi1rBvVb+CN738RxeIVrk+RVH0vzqRAMELJVuXP8Crt/UUPen70/nfmMOGMhpDITTYPnk4rhz8eR8W0plAV1ESihzi51ZJK2eRDNKw+h2kXvZ/VdjjxqsaedvtqjDYPM696g02/vzjqPGSzL+8W+zjMjXtZXNiT+DykihTg9GWr4o64x/tcHP8v70fv85SDnHrr05jsHiSZhDMW8c6P6pdoePocmj6cFXptslmEkVShViAQCHLJYLm+7ZMaiiccAVnDaPEBYLT48HRaObq1lrIph7D0tsXLN9dDbC+XT21EMfs4854n2f/2nJCXU/V9upXnX+4uZK7FnR3fJ3E9xP5sTDxr84Bcn/j82KLep5HeNz7fEDn2AoEgKbEKsHi79aq7skGj9nM7QYoM/jEVuPA5bVHLaZo+GKAGpFAxHse+MXFzq4OhYq6WkhSLvqVOvKqxBpNfr95qUCNyAVMtPJdWNVpNY7bVgyMg02QsHliOuaRy5j1Pxsx9DNtg6ufC6sVk9xDw6RcGwQuWM+9ZyeQl66LWEev8SLLGpCUbInL4khcgEggEAsFgE8/1ni4rxeOOIRvU3rzoPhSLl7En7sXfEzmYkE+uhzDHIYUcZ7T4kCToPFTO/ndOjMj7T8X36Vee7/N93Bo3qTDErofY58dg9tG0/viIx4TvBxdxYy8QCFJAL8DidZpRAxLeHiMBn8JHv72kVwQSBqXvxk1T9V63O9acGSrc0tlUpofyabB++aVse3YRAAG/QsOq8yKKxhjtrtANdXj+VvnURjzdZjoOVGatyFosOQUHKaLllkLhuQTrjXVxUunvwiqp/PBoBSvHnJZR8Zzg+aqZuwt7hSNugaJkxX1i7bPqN6CY9RD95K1yNMqnHMTrNNN5sALVLyPJYK9wUD13d2hfEhYgEggEAsEQEdv1m1eej6bK7PnHyUiGSN9JikrD0+dgMPvy2vWgO87XEx3NYCvvinEjm5rvU+80A/VGX8j3mRTGzR/Xg9HeQ/nURrxOM16nfk4Vi4/qubsi9kX4fnARZ1UgECQlGEq16YnzceyrpXhCM3Nv+DveLjuvf/fLzLn+b1TNOBB6fcvuOkrGH6NqziehEKz2vbVUnPAJc655HW+3nfa9dRzbfhwBn0LAY6LrUEWoaMy0S96NDpmboofMaZrEoU9rObp9YgbhXLHy0frk7fcYsZV1hgYp+ofeFdYcSyOkrG+9rtYirOWdYRcFffvQpti5prGOHk1mMaRdKM9odzH9X9+m9uSdaKoU6lgQDK0z2ryhEMNkBYNi7bPB5I14RaKZE/1CzY0kBzAXQyAgISt6F4XZ17yCJGs0fTidnX85MysFkAQCgUCQPRK5/o17b8RW2cbk89eD3Hdzq6kSfo9pGLhef1wx+/C7jchKADmO68PPRXLfp+Z6gEafMeT7s0nP9/nkeoDjL38Lc1EPPpcSapUkSUHXI1w/RIgbe4FAkJRgKFVw1DVcRmpAonLaQbzdZnpaSrCWd1JQ5eCNe29A06SI5Y5umc4bu48LFUtxO3oLgPSOLgdzxWLlbxmtPtwdNna9eHpINmlVj41T7bW/vOd//U9UTjsYej5cbonOQ39SuyjQCEgGevrlx6WTHzl5yTpqT95FwKtgCAuRNFm9aBq07qkNhRj2zYhsYMZVr+sFg3r6CgZF7/NnnPy1F/A6zfQcK0l4wRI8P6/dcxMn3fBXKqY3RuRZSgaVgMdI7cm78LvNGRdAEggEAkFuSOx6mVlfeA1ftwW/QQvdEGoBmZad4/Pe9dDnuA2/u5i6BdsjKrX3v5FN1fcpDwBoGj4kfFqkO1P1fT65HmDrqvOwFHdTPrWpn+s14fohRITiCwSCpCQKpapbsAPJoPLJKydHhJZZy7oIeMwphWDF6oMaM7xt1/gI8aYTzhWv12pk/pdGwRgHni5LzFzAdELKkuaV9V7gSNrAwguD+XIBrwm53y96+96xdDVVcuY9Kzn1W6spndwEQPnUgxSMbePMu5+MyJ/rv8/ergL8bhMfrzw/pbDB4PlZv/wKHJ+NjXhOliHgMSUM7RMIBALB0JHIcWWTmiisaUOx+Ni84oKQEwwmP7byrrx3PYQ7bnxfmP3+Sjxdlqgw+1R9n1IOuaRieuN05AGkE+ST60E/Px8+dpVwfZ4hZuwFAkHahLd4qZr9CZKk56Mf3ToJV2sJ7z74BTydhSmvI3YfVFPK4W2Zb0Ov9hoUdTartyarVBy8+KjztqW13ljE6hMb8BmwlnXzwSNn6TMil7yHOaxgEEDnoYrQCH//cwFB+X8xpQiFcIz2HqzlnQR8MgZjX46fY38V7XtrMdpdIjxPIBAI8pwohxpUju0Yj6utGFuFg86DVbxx7w0Jb7rzzfWqX0kzrS4xqXQlKJvUhNSxjZmWMra4LWmtPxzhekEyxI29QCDoR3KZJmvx4usx89p3vzygdRzaMGXA4k23FU06ofaZEOvi4+TuffxD0X+KrQFvosUToFE+rRFNhe6jpZjsbvRcQi+FNS00rZvBpHM/gqKeiKWs5Z1h52IWO9acHfF8pm2Ugrl39FZClmS9QnLVrL2MmblvQL2IBQKBQJANMnN9aX1zhEPLJjfpYe9prGOkux6ifS9pPq4o6uRY7zYyu+kVrhckRtzYCwSCPhLkpoWTqMfr3tdPov7sTb1FW+oyWkfD0+fSvHEa25XAgMS74/kzqZr9CbKihkawE/XEzVY/4HjEuvgY52nj5zUqhbLKxi4jLcxKspZoCmtaMCgBNvzuYo5unRy6MFr/q8t6wwE1DGYfPpcRgzFGwaBV5zDxzI/1CroJ3vdU2brqPFxthUxe8lGowI8k6cLf8/eT+fSVUwa8DYFAIBBkSBZc3/D0ubjaipiwcEtC349G10Ns38+xuPl5zWEKZZXGJevSvukVrhckQ+TYCwSCEIly0/oTKy/OcWAMhVXtoTC4qBYsMddRG/FYsIBNNlqklE1qwlLcg99tirmNwSZW/1ir5iegSTzcUs5rxccnWUNsgrMPR7dOBvoujDoPjukNO+xNMfi/i2neNCVi2dbd43C1Fqf8vqeC6lf49JVTaN4cua3mTVP47PXPZWUbAoFAIMiMgbreYPLR/ll1RNh7It8nagk3El0PsX1faNB6fV+W0Uy2cL0gGWLGXiAY5aSamxZNX7sUSVYx2jxUzfwMX69YE4XBha+jYvoBgvXj/C5T2rl18Y7HUtbB1As/QFZU7GMc+tY08LuNVBy/H1tlKz3HyuOsJfPtJyNWjtwml4VXu+08eWwtG6WpaY+kx5990E9sX9ihidlL/4mny4q7w461tJPyaQdQLJ4U3vf0zonqN1B6XHNE3mRpfXNWQx0z3TeBQCAYbWTD9a7WIgprj6FYvZx229OhNmfJfa+vw+9WMJj9BDxKTl2vBiSQtCF1PcTzvZnLi7r4JINZc+F64fpkiBl7gWCUM3nJuqhKqkFJn3nPyohKquGUTmpCsXjZ+fxC2j+t1dudSGCy6XniJpsHTZVjVkY12l0UjTtM3SlbMVo9SJIevuU8VoRi9VA9d2fcWf5Uj+fkrz1P6XHNUc8rFh+mAhfzvvRi7BX0VqtHUmM/P2D6LpIc+6rokYzMsbqZZXFT6evK2kh6+HEEZ0SCo/mbV1xA2546FIsPc2FP7wVWgvddUjn9jqfSOifh20q1yu5Aj1MgEAgEscnU9YU1LSHXb175L3QcGIMkgcEUCM1EJ/K9rbKNulO2olg9qAEDkqTfDCpWD3WnbMVWmVkB2USul2QNSWKIXQ/hvldLP4cjIPM5m5vjTD7h+gEcpyA+YsZeIBjlJMt/ixfGNnbObgwmH9MvfSeUP6YGJAxKXzuXeGFwk5esY+JZm/WB17DuL8XjWkCCE697mX1vnZhRqFr48ViKnQChHqvB3K/Dm49j90tnxCxeEx6imKhGQHwSjyiHV97vbBqD55fv8S+OrVxX4sCAluIMSnJiHUd4waDW3RN6z9N7WIr1Qjvx3veyyQcpqmuhbHITbXvGpbT9wShOFO84BQKBQBBJpq53Hi2laf00Zl3zakS+OFJk67Z4vj/p+pcoGtcS4Xpj74DArKtfo7OxgvceunZAxxPL9QA9bXY2Pn5JRDX4ILl2PfT5fssfz2Xm6v+h528nMkbRUCSE67NwnIJoxIy9QCBImP8Wjx1/PoeGVYsj8scMioamgvNYYVQP+Ihl1yxiy9Pn4XOZQwVXQI/q8/WY2PLH8wZUSTXW8QTRNCibdJiF33kqNFJttLtC+YGp5gzGJIUR5fD+sZOXrOPCjm3IwCyLHumQygxKPOIfRzu2CgcGsy8il1E/T5HyDr7v4euaeuH7SBJMuXBtyuckG3mT6R9nmu+XQCAQjCIycb3qV9j+p3Oj8sVT9f17D11L4wcnAPQVWJMBJBrfPyGjm/pExxOOYvFzyjf+FPLpYLoe+nxfNukQplfnUm3U8Gr6YIBw/UCOU7g+Hnk5Y79t2za++tWvsnfvXq6//np++MMfIknJcyquvPJK/v73v4f+XrRoEX/7298AeOedd/jmN79JS0sLy5Yt45ZbbsnZ/gsEww8toz6ysfLHWnaPY/0vr0jasibWsgDHttVnodhN3/GYCvQf/uBPiCwTChkMbifdVjnxSGVEWfUrGMw+bBUO9r9zIpbzPqXa30VwbNtk8+DusCWcQel/rMH3KPZxHOTMe1YCsP/tOWHHoYGkUn3Snpjv+9SL3mPcqdvw9ZgwWvVBh9IJhzn11qcx2rw0vj+Dbc8sTmH/sk+23i/B0CJcLxAMNpm5Hgbm+61PL6F8SiP2iq7QYz1thWxdtSRrxxN0PfT5vv/M9PGXvxnTkblwPYDB7MNo9bD/nROZcKmK3LkFm0Hr27dBcT0g+YXrRwl5N2Pv8Xi46qqrOOmkk3jnnXfYuXMnTz75ZErLbtq0iQ8//JCmpiaampp45plnADh27Bj//u//zlVXXcXrr7/O6tWreeutt3J5GALBsCLzHKmwfPH9VXi7LRRUtQNaKBxLb8ESf1lN1UP41YCEpkLZlEZijfpncjz73pkJgOqLvGBxHiuOEGms6rWJcgbDyWREWc8NXMGptz5Nqb8n6vmAV+HotvrkB9pv1mDHmkU0rD4bo80ddhxeJFlj72tz+46jd7m6BduRDCp7X/1c1Puu+hQ9R9HuQe5Nr5AVDZNdr4mg+oZuXHgg75cgPxCuFwgGn4HlQw/E9yrW0m40FfxuBU0Fa2kXMLB86f6u93ab0AKRtzbhEQmx3eFFU6Ucun4lp966Cno+i3o+4FVo31ubfOY5Ddd7uy3seP7MiGUXfX+FcP0oIe9m7F955RU6Ozv50Y9+hM1m49577+W2227juuuuS7hcU1MTmqYxY0b0qNfq1asZO3Ys3/nOd5AkibvuuosVK1Zw1lln5eowBIJhRaY5UuH54q27J0SN2ifqC1tY04Ji9RDwKmz43SUgacz70t9QLN64o/7pHo+mQXdzOXNv/Ae+HiPOo6XYKjuwVXbQf4Yi1mxEKq1y4o2cJxpR3rFmEQVjW6mY3hgxR6L1/o+topPJSz5gx5qzE2471qyBq6UESY4cGJEkKJvUjK3CgWJ1UzzuCIU1rdSdsg1J0gshHmmYhKu1hHcfvBpPZ0Eof3HmVa9jMAVC61L9BrY+c86QtRAKkun7JcgPhOsFgsFnIPnQA/F91ZxPkGSNnS+cxmevn8xxi9cx/ZL3qZrzCUc+njrg49FdX8FJ1/8Dn0vfD7ejEGtpV1REQix3dDVXZOj6Jk791iqMdm9c13ceqmTGlW8g+90Rz2ka2Mo7OfOeFdEz7P1Ix/XmQhfVc3fjPFqCBNjHtGMt6RKuHyXk3Y19Q0MD8+fPx2azATBr1ix27tyZdLmPPvqIQCDAlClTcDgcXHjhhfzsZz+jtLSUhoYGzjrrrFCI37x58/j+97+fcH0ejwePxxP6u6urK8GrBYLhTfwWKolJ7SIhKNTIG2nn0VLe/MENaAFDaPnXv/clJEOAgCeyF226BMPfACYubECSoKu5nC1PXUBpfRMnXPFWxOCB3jbHTfnURgI+GYNRJeCTKZ92AFtFOz6XJW5hm9gFiby4HeEhdtFhjuuXX8Gp31pFaf3h0GMS4HWakU0BDq6bGXN7sVoWHXfu+tBIfv25HyHJ+kWDpkqh6sDFE5o57fY/6qF2vV0ISsYfAaBsykFOvXUVRlvkxUnTuhOYceXrEdvXNGj68ITU3oicknlIqWDoEa4XCAafTF0Pqfg+tusBWnZO4PX/+iKejkIAPnt1Ac0fHR+6Cc+UcNfXzd+FrGgYrV4anl5M+2c1FNYeZfbVr/XzfQ/lUxvxdJuRJA2T3UvppCZsFe2AFLeQXbzig6pfpmHVOTStmxnz2OOlHQaL+e75+wI+feWUqO2l7XpJQ5L1+gWzr3kZuTfkXw3IodoGuuuf7nX9HOH6EciQ3dh//vOf55133ol63GAwcOWVV4b+liQJg8FAe3s7paXxQnxgz549nHTSSTzwwAPIssxXv/pV7r33Xh555BG6urqYPn166LVFRUUcOpS4zcRDDz3EAw88kMGRCQSjh6QXCZLK6cue5r2HP8/pt6/mvYeuDvVtHcgFRjIiR9b1EeiC6vbQLPqB92ZEhAzqr/8YSdLwe40YjCpqwIC5sIcz71mZdDQ94QxA8ByEHbuOhqW0Gx9gDHv02Pbj2Pans+POoMSaNaiY2siYE/YDoPr7thG8qQe90JFs0PPntICEpGihsDuT1YvfrUTl+hXWHMNgDODrMeE8Wop9THtvRMUxug6NiXs+BoNks0eC/EC4XiAYGSR0dgLXAwQ8ZgKeSL+7HUUD3qdYPvR7FaZf+k4oYq7/ZMPxl7+Fuaind1BBF6RBCXDa7atQLL6EeduxXC/JKq7WkoSuL5vchGYsp9PVSnHYKWzeNIXPXv9cyscWy/WaGul6IOR2/b/70h1MVi+aBi07x0Uco3D9yGHIbuwfffRR3G531OOPPfZYVPEcs9mMy+VKKPtly5axbNmy0N/3338/1157LY888giKomA29/2gWCwWenqi81r7r+8b3/hG6O+uri6mTs08XEggGI0Ew8cmLtwyqG1K0m3rs2PNIrqPlDHlXz4ItYMxWny4O2zs/tspHPxgdtiro0eJE80AFE9oprC6pd+xa72y8uKVjTh9AToDMuNMfsqnH8DvNkVtI9GxGYwqXqcJJCmUhxacDQhH9UsEfKbQa8I5smVy1HmRDCp+t4mNj1/UJ9QbX0IyDLSP7MBH2gerxY5gYAjXCwQjn/xyvTdhC7+tq87Dsa+ayeev72uTJ+uh5w1Pnxm2TCxPaZRNOYin24zHUYC9qh2DUc+1Nxd3UljdwpgZn3F066TQ64M3pr75f8Tz9uV0B1SsskbAZaa0vjmus1J1vWyAgF/GEHYDL0lEvS5I+96xrF9+RcRjwvUjhyErnldVVcWECROi/lVVVdHSElnEo7u7G6PRGGdNsSkuLqalpQWPx0NpaWnEOru6ujCZEof6ms1mioqKQv8KCwvT2r5AMFoJFpgpHneYyUs+RFZU6hd/hKyoTFqyjqJxh3PUpiTyLjbdtj6Na2dHt4PZNS7ypj5Oi5vgDIAkEyrHa1ACnLbsj5x43SvIitZXZKeyjTPufArnsWLW/+oyDJrGD49WcENTLc+VnohB8fcrZBRdSDDWsR3bVs+xbRMjz4gq9fvbEPUaT7c5bqui7uYK3rj3i7TungCSyvR/fY837r2B7uaKqH2KT7/9T7FNUDJy2WJHkD2E6wWCkclwdb3qVzjw7onRy+wKWyaOpwprWjDZXRitXsxFPWiqfhtVMfUAJ/6H7vrjL38ryvVv3HsjmrUKq6Tyg6OVXL5/HB+vXBKjcGHyY4vp+oBMwGfo97rjol4X8BmwlnVHbSfC9UDr7vH0tBbS3VwW6xQmIGy9wvVDQt5VxZ83bx7r168P/b1//348Hg9lZYk/XEuXLuXDDz8M/b1x40aqqqowm83MnTs34rktW7ZQU1OT/Z0XCAShKrALbnmW8mmNAJgL9VmzimmNnPrNP2XUtzUhMQUSVsF3X1XCPruxX2+heu4ekPqKyYQXsAln66rz2PbsIlSf0jdjLuuz/qFlgz1r736SwupWSiYcpfPgGB4bu4iNbj2fb4+tmjfu/WJfmkBcMcY6tsaIx7xOk14ht8eo/91jxGD0Uz7tAF6nGTUg4esxofqMbI55gREp1OCxF9W2pi7UGPsf7xwKRhfC9QLB8Gb4uj7WMpG+j+cp59FSXrvnKzSsWoymyShmPwCKxR+amDbaPVGu97vNaAVTuaaxjo1uK05N5tj2SZGdBFI+tv6uN2Mw+vG7FTxdVroPl6BpUD59P+XTDqCp0HW4FE+XFb/biGL2JnR96PjHtlMy8WjG741w/dCQdzf2Z5xxBh0dHTz11FMAPPzww5x99tkYDPpIVGdnJz6fL2q5mTNncuedd7J+/Xr+/ve/c99993HTTTcBcNFFF/H+++/z1ltv4ff7eeSRR1i8eGh6MgoEI51gexK/2xLK+QoWbpEk8PWYs96mpE8gTaHH0m3r0//1n746D8mgMmHhlqQtbuLNAGhE9tNFUuk+XIYk6zP4lpJubKqXIrlv8CB8JDqeGGMem8WLYvWEHtv9t1MB2PnCQtb+5Go2/f4i/B4FxeTn45Xn8+pdX2Xj7y9EMfvwdhXEbFWUSXufWO/LmBmfDWg9gpGHcL1AMLwZrq6PtUzQ9/XnbEjqer/bTNO6GbTvHRuxzkSut1U4kFxNKFLkYEMy36fi+s0rlxDwKsiKyuYVF/D2A/+PDb+7CIPRj2Lys+F3F/POA9ezeeX5yAaN9b+6LGZbwmz5vnrubuH6IURyOp0DaxidA1588UVuuOEGCgoKUFWVf/zjH5xwgl6Z8fjjj+fBBx/kkksuiVjG5/Nxyy23sGbNGiorK7nmmmu44447UBT9C/Ob3/yGb3/72xQXF2Oz2XjzzTepqqpKeZ86Ozuprq7m2dKvYZOzU+BLIBjJzPmPv0dVgQVo+nA6H//hggGvP7xi7PGXvU3V7L34eky89/AXQNPD0gIeU8QotGLxoPoNMWecZcWPuag7dGESXKfzWBHWsi58PSYkg4bJ5sXbY0bzy/3a2Wmc/YP/w2hz6S1jelPLwtOINU3P4zMYA6F1KEVu1nQUsLytjMVnnMDnrhsfdVyHt9Szs7cvrc9lJuAxIiuBiGMz2vWZEp/T1ns8PkwFLrzdttDx9n9NsnNy/OVvRhTvMdk8cY498fuS+jkUpEOP6uGq9uU0NzdTVDTwQlSDjXC9QDD8GW6uh/i+11S9M42kaJisiTylcd6Dv0Qx+yK6zwSJ5XpTsY/nHFaWt+lRSbf/9PKYxxbu+1jHFu1xDVNBD5omRbh9qHyf+jkUpEOqvs/LG3uAQ4cOsXHjRhYsWEBlZXaqHn766afs2rWLM844I+2LICF7gSAd9JtcWfFjtHqRZBVVlfG7TKh+hTe+/0UGWlAlloQ0DXw9JhSLPw2B9BV3iSc2SVJRLL4Icbsddnb99bRQTl5hzTFOueVPeLrNfPrqfI6/9F1M9r6iNVpvdJ0kR67jnxPr+dmmTgAWn3ECX/hDc9pyjSJudd70qV2wjWkXrw0VGYp17OEM5BwK0mO439iDcL1AMLwZnq6Pt16/R8Fg9Ed5ur+nCmuPcvodT/PZG3Oo/dyeCD/Gc7187k/4lz/1deC4/aeXZ3QzHUEWXQ/Z8X2q51CQHqn6Pu9C8YPU1NRw8cUXZ030AJMmTeLCCy8cthdAAsFwIRg+tuuvpxHwGtn54mmoXiO7/3Zq0hC5VNmxZhHb1ywEiYhK8AZjgF0vnsr+d05MHvbVLycsGFqoaXJonSabh4DHRPtnkWF3/YvzOI+Wsnnl+djKunG1lNL+WXXE61t216GqctQ6ttojw/fj7YOmyimHNaaW25bamG66hYkGcg4Fow/heoFg+DJcXR9cb39XKWZ/VLX1WJ4yFfSACse2TaajMfK3K57r1Qn/EfPYBuL7bLoesuP7VM+hIDeIkoICgSDrBNuTqH4DhzdNw+820/jeHFS/geaN07PWpsRe3oGpv9BljfpzNjLtkveTjnaHSzHYmidWn9rW3eP0FjddVlytRVjLO0PFeYx2dygUbfxpW3tzyjZQPP4oXqeZntZirKVdFFS14+u24lcCuFqK+9ahTYzar3j7kEiM4SFx4bltbkcBoIfw+5x6kb70Rvn7ivf0P/Z4MzHpnsOBzugIBAKBYPAZzq6H2K7SNJK6fuLCLb3dbtJxfewb7KZ1M6iYdoDaz6Xm+9y5HrLl+3jnULg+94gbe4FAkHVUvxLK4Qr+fzBHLJstSnasOYva+Tsw2jyhsC+DouLrHe2OJcbkUjRFia18+gEMij+yx+sNf6ewpoW6BdsiQtEAyqccRLH40FRo/7SGXdsnMvfGv4EEG393ccQ6Kv1dMY5Ml6uvx4Ri9eJ3mZKKcfKSdVH7EazE3z+kL95FTiyCMzIbH78w6ti7DsWbZY2+OEh0DuOvRyAQCAT5yvB2vRmf0xLhKtsYB0aLhw2/PZ/W3ROz6nq1c2uco9OomHYATYPuI2WY7K6Evs+V6yE7vk90DoXrc4+4sRcIBMOWwpoWJBla9tRROe1g6PGOg5W9oo8WYzIpHtowpVds/xImpZdY/6vLcOzTW2e17poYmqXYsWYRnYcqmXbxWsw2fUbBaPXhdtjY9dfTQxccb953A9BXyCa4jrY/fBqxf0a7i5IJzSgWL12HyiibdJiu5jIKa9qoPOEzHPur+0bjw4i1HyabB7fDTsPT53J0Wz22CgeQwih/GMEZmeDFWvixJ3pfoi8O4p9DgUAgEAjikQvX7397NgfXzYhwVeUJn3Li9S/j7bYD2XX9mVdPjdi/4MCDfUwbRqsHSQLn0RL2bpnHCVe8RemkJto/jb4Zz5XrITu+T3QOBbknb4vn5RuioI5AkH/Iih9Z8bPwrj8gKwF9tLiig4DPwBv33sjpy1bFDEFLVCCmeeM0ZMXHgm/8ObRssgq7EF0ZONWKwB8/t4tX390OhBfP+7j3YkQLK6Yj9V6MzEkYchhvPwZcpCcN9PclkFaVYkH6jITiefmGcL1AkH/kwvVNH85AVrycettq3vvfpaFlk7kqU9efc/ABLnr4+NDfv9tX2utkU6+TwyvIu9n/zix2/PmcuOvLB9eD8P1gMeyL5wkEAkEyVL+Ctawros/rphUXoJj91J28I25RmUQFYlS/QlFdS8Sy4b1mY9MXiubYV4WnyxoWSpceejGaxWiaoV8xHQMNTy9OIuQ4+yEFKJvcRMPqswdUlC9Vgr1+w0l+DgUCgUAgiCYXrgcomXiEwrHtEcsmdlW2XX9ur+u9QLiTFye8qU+0HzuePxO3w4amSTl3PQjf5xvirAsEgmFNeOiY0e7C1VrCBz+/gqkXvp8gBC1GgZipjdgq2gEphfC1yLC/zPLS4pNJ8bxE+xG88HG1lNK6uy7t9QoEAoFAMJQI1/eRaD+MNjeW4h5aP6nFUtyT1noFwx9xYy8QCIY14cV7Ui0qE0uKn7vpL5x5z0p8TkvigjQxqsxmkpeWmPQr0/bfj9gXPhsomXAkooKvqFYrEAgEgnxHuL6P/vvRebCKD35+BWpADg1WlNUfwus0424vxFLSLVw/ShA39gKBYMSQrKhMcLQ6lpxfu+crVM/dxZR/WZdw2VhVZsMvOIL0D01Lh0xnBZJd+MSs4Cuq1QoEAoFgGCFcH7kfsXwvGwMEfDIF1W0caZhIxdQm4fpRgLixFwgEw4DUR5lTCW2LJ+fGtbMpm9QUtexAqsxmQjZmBVKt4Cuq1QoEAoEgf0jN98L1fcTyvSSB6jPRsEYvFBgsaCcY2Ygbe4FAkN/ECIdLTGahbYmWTadvbDbI1qxA7Auf8REXPgOZbRAIBAKBIGuk5Xvh+nCSDXQI148OxI29QCDIa2KFwyViIMVt4i17cN2MlML+Bod0cuQGcuEjEAgEAsHgkY7vheujXy98LxA39gKBYJBIXS5GuwujVW/Tkm443EBC2xIt23WoMqPqtVklzeiFbFfwFQgEAoEgObn3vXB9JML3AhA39gKBYDBIU1IDCYcbSGhb4mWHfjQ83eiF7FfwFQgEAoEgAYPke+H6SITvBSBu7AUCwSCQrqRSrXg7mAzVaPhAoheyXcFXIBAIBIJEDHffD0fXg/C9QEfc2AsEgpwwUEmlUvF2MBmq0fDBLuYjEAgEAkE6jCTfC9cLhjPixl4gEOSEgUtqqMLhwtff999DNRqeb7MZAoFAIBCEMzx9L1wvGHmkVpFBIBAI0mTHmkU0rDoXTZMx2fSRfJPNg6bKNDx9btKR52A43OYVF7D2J1ezeeX5KGYfhTUtudtpSeX0O/4Ikhr530NM07oZtO6ODGkcyugFgUAgEAiCDDvfC9cLRijixl4gEOSMgUgqGA7XunuCvlxvOJzzaGlO9hUicwPD/3vo6ZvNcOyrwtNlDZvNEAgEAoFgaBlOvheuF4xURCi+QCDIIZmH1w1WOFx4bmD94o+QFZVJS9YhAbKicty561POE8wVoo2NQCAQCPKb/Pa9cL1gNCBu7AUCQc4YDpIKzw1UrF4AKqY2hp6vmNo45MVrRBsbgUAgEOQz+e574XrBaEDc2AsEgpwxHCQVXrDGYAwAIIUlKRmMKj6nPKTFa0QbG4FAIBDkM/nue+F6wWhA5NgLBIKcofqVKCn53eYocQ01sXIDwxHFawQCgUAgiM9w8L1wvWCkI27sBQKBoF/BGk0FTQXHvjGieI1AIBAIBCMC4XrByCYvb+y3bdvGwoULqa2t5e6770bTkn/JfvjDH2K326P+vf322wBceeWVEY9fdNFFuT4MgUAwTAhvtdOwajEBr0LAZ6Rh1Xm5b7uTwu+bQDASEa4XCASDiXC9YKQjOZ3OvPqkeTweTjrpJBYvXsy3vvUtli1bxmWXXcZ1112XcDm3243b7Q793djYyMUXX8yWLVsoLi5m0qRJ/OUvf6G2thYAo9GI3W5Peb86Ozuprq7m2dKvYZNFvotAMJKQFT+yEsDvNof+G0D1G1D9CorFE/rvrCKpzP3lE/zH7hJUJBafcQJzrpiW3W0IRiw9qoer2pfT3NxMUVHRUO9OWgjXCwSCwWYoXX/Birc5f1Mbam+HgNt/enl2tyEY0aTq+7ybsX/llVfo7OzkRz/6EfX19dx7772sXLky6XIWi4WSkpLQv1//+tfcfPPNFBcX09TUhKZpzJgxI/R8OqIXCAQjm/DcwOB/h+cG5ipPsGxSE5W+LmZaPANYS16NzQoEKSFcLxAIBpuhdL3UsU24XpBz8u7GvqGhgfnz52Oz2QCYNWsWO3fuTGsdzc3NvPjii3zta18D4KOPPiIQCDBlyhQqKyu5/vrraW9vz/q+CwQCQTKMdhe2Cge2CgfHLdqEAe3/t3fvsVHWex7HP9MLBXqjxVop5wBy6aEF1pQuNOtyjcSoWLKGVgjFJbDAEo26iQMo/lNpJEoksSyIMbqJmO4Kf2gVjxBQoacna2zdNsBaoF1xy9mW47GFQy+0Q0tn/8AOtGWmM9MZ5vnNvF9JI/N0nuH35TvPfPw981y0MqlNE2J6NK63U7HxXd6/mK1Pf7/13yVbX/AGDAQBWQ8gnA3Oepuzx5X1E2J6yHoERcguVblq1SpVVlYOWR4dHa2CggLXY5vNpujoaF29elUpKSlevfb777+vwsJCJSQkSJIaGhqUk5OjXbt2KSoqSlu2bFFxcbFKS0vdvobD4ZDDcXvPWnt7u7elAYBbd95L1xZzK6QfGt2tf834s5J+vqxLj7Z5ff/c1GlNSpzQqtRpzbryP/1X+nVKvx7qB4QaWQ8gEnnK+sSoPv3p0e/IegRcyM6x//nnnwecJ9dv//79stlsevPNN13LMjMzderUKWVkZAz7uv1767/88kvNnDnzrs+prKzU2rVr1djY6PZ1Xn/9de3atWvIcs67AzBSE/N+0O+e/E+NTu50LWvpjda392Ur+h8f8bhubHyXYsfcmohk/cMflP43F/XnM1N1vnyRpD7N/aff64+7iySn5Q7IQoCZcI49WQ8gUrnL+g+ujtOckmc8rkvW407e5n3IvrFPT093u7yurm7Aso6ODsXGxnr1uhUVFRo/frzboJek5ORktbS0yOFwKC7u7sFtt9v1/PPPux63t7crMzPTqzEAgCdN383SfZmXNHHeBdey2q7R+u/43+ihYda927cAqdOb9Hf/ckix8d2y2ZyD9uoDoUPWA4hU7rL+eEeC5gyzLlkPf1huN09ubq6qq6tdjxsbG+VwOJSamurV+p988olWrFgxYFlRUZGqqqpcj2tqapSenu426CUpLi5OSUlJrp/ExEQfKwEAd27fS7cpNll/vRmlh8Z0e3U7nHOfLtHZjx+R0xmlUWNv7c3v/29783jZoqQpS2pc5/b5dB4fcI+Q9QDC3+2s70v529tZ78WF8Mh6+MNyE/sFCxbo2rVrKisrkyTt2bNHS5cuVXR0tKRbt6Lp6elxu/6JEye0aNGiActmz56t7du3q7q6WkePHtXOnTu1efPm4BUBAB7ceS/dg/c/rF1/uU9jbH1K6/Xu/N6m72aptX7gXvqY0Tc0Zvyt9fv36i969aCmP/pdwMcPjBRZDyDc3Zn1N5b+wZX1U2Pdf7bdiayHr0J2KL47MTEx2rdvn9avX69XX31VfX19OnbsmOv3eXl52r17t/Lz84ese/HiRV2+fFm5ubkDltvtdl26dEn5+flKS0vTpk2bZLfbg14LANxN519SdLJ4w6+33bmg/+oeo6I//UYLfhuv4c8ulu78FqCrNUkJD1xR9KheRY+6vVe/+6/xOvsfj6ipalYQKwH8Q9YDCHcDs16urL/h9Paid2Q9fGO5ib0k5efn68yZM6qpqVFeXp7S0tJcvzt37pzb9aZOnaq2trYhy2NjY3XgwAEdOHAgKOMFAF/09cYMuVdupzNKN23RXq3f/y1Azb89odb6yRr/u//VvH/+XLao24f3tdb/lqCHpZH1AMKZu6z3FlkPX1lyYi9JGRkZXl0ZFwAizeBvAVovTJajfYyiYm6qqzVZY8a3KXXG/4nb4cDqyHoAuDuyHr6y7MQeAHB3g78FuLVXv3fAXv25648qMaNF7c1pHl4JAABYEVkPXzGxBwDDDd2rP0Unizeor9e7Q/sBAIC1kfUYDhN7ADDc3c7j6w9+AABgPrIew2Fi7yXnr/eXvu68IfWFeDAAwkZ3Z496u/tcf77e5wjxiGCK684bkm7nE0aOrAcQDG1tba6sl0TWwyfe5r2ts7OT/yPwQlNTkzIzM0M9DAAABqivr9fEiRNDPYywQNYDAKxquLxnYu+lvr4+Xb58WQkJCbLZQn/lyfb2dmVmZqq+vl6JiYmhHk5QUWt4ipRaI6VOiVrvNafTqY6ODk2YMEFRUd7fQgnuWS3rJWu81+6VSKk1UuqUqDVcRUqtVqnT27znUHwvRUVFWfIbkcTERCUlJYV6GPcEtYanSKk1UuqUqPVeSk5ODtnfHY6smvVS6N9r91Kk1BopdUrUGq4ipVYr1OlN3rOLHwAAAAAAgzGxBwAAAADAYEzsDRUXF6cdO3YoLi78b3NBreEpUmqNlDolagWCIZLea5FSa6TUKVFruIqUWk2rk4vnAQAAAABgML6xBwAAAADAYEzsAQAAAAAwGBN7AAAAAAAMxsQeAOC11tZWZWdnq7Gx0avnFxQUKD4+3vWzfPnyII8QAACMBFlvJib2hvB1A5OkyspKzZ07V5MmTdLevXuDOLrA+eGHH7Rw4UJNnDhRO3bskNPp3bUdTflA8ac+E/so+VerKX0czNft09SetrS0qKCgwKfPodraWlVVVampqUlNTU06fPhwEEcYOF988YVmzZqlpKQkLV68WOfPnx92HVP7Cusg6z0zJSPIes9M6ePdRELek/WeWbmnTOwN4M8G9ssvv+jpp59WYWGhvvnmGx06dEgVFRVBHOXIORwOFRYWKicnR5WVlTp//rw++ugjr9Y14QPFn/pM7KPkfy9N6ONgvm6fpvZUktatW6eCggKvn9/U1CSn06lZs2Zp3LhxGjdunOLj44M4wsC4ePGitmzZop07d6qhoUGTJk3Sc88953Edk/sKayDrh2dCRpD14Zn1UuTkPVnvntV7ysTeAL5uYJJ06NAhPfDAA3r55Zc1ffp0vfLKK/rwww+DNMLAOH78uNra2vTGG29o6tSpKi4u1sGDB4ddz5QPFH/qM7GPkn+1mtLHwXzdPk3tqSTt27dv2NC70/fff6+bN29qxowZSktL07p163T16tUgjjAwLly4oOLiYq1cuVLp6enauHGjampqPK5jcl9hDWS9Z6ZkBFkfnlkvRU7ek/XuWb2nTOwN4OsGJklnz57V4sWLZbPZJEm5ubk6ffp0MIYXMGfPntW8efM0duxYSdKcOXO8OiTGlA8Uf+ozsY+Sf7Wa0sfBfN0+Te2pJD344IM+Pb+hoUE5OTn67LPPVFlZqcbGRhUXFwdncAH0+OOPa+PGja7HDQ0NmjZtmsd1TO4rrIGs98yUjCDrwzPrpcjJe7LePav3lIm9RaxatUoZGRlDft59912fNzBJam9v1+TJk12Pk5KS1NzcHMgh+81drQcOHNCUKVNcz7PZbIqOjh72A9+UD5S2tjaf67NyHz3xp1ZT+jiYr9unqT31h91uV3l5ubKzszVz5kyVlJSovLw81MPyyY0bN1RaWqpNmzZ5fF4k9RX+I+vJ+ruxch89iaSsl8h7d8h66/Q0JtQDwC179+5Vd3f3kOUpKSl+vV5MTIzi4uJcj0ePHq3r16/7Pb5Aclfr/v37XXvA+sXFxamrq8vjv4Pdbpfdbnc9Likp0dq1a1VaWhq4QQdATEzMkIvKDFeflfvoiT+1mtLHkTK1p4GQnJyslpYWORyOAf8GVvbaa68pPj5eGzZs8Pi8SO4rvEfWk/Xu1rFqHz0h6z0zta8jRdaHDhN7i0hPTw/o66WkpKilpcX1uL29XaNGjQro3+Evd7Wmp6errq5uwLKOjg7Fxsb69PpW/UBJSUnxuT4r99ETf2odzKp9HClTe+qPoqIivfjii5o/f74kqaamRunp6cb08+uvv9YHH3ygkydPDvvejaS+wn9kPVnvbh2r9tETst4zU/vqK7LeOj3lUPwwNXfuXFVVVbkenzlzRhkZGSEc0fByc3NVXV3tetzY2CiHw6HU1FSP6xUVFQ2o1aofKP7UZ2IfJf9qNaWPI2VqTz1pa2tTT0/PkOWzZ8/W9u3bVV1draNHj2rnzp3avHlzCEbou59++kkbNmzQ22+/raysrGGfH459hfWZ+L4j64cysY8SWT8cU/vqDllv/Z4ysTecu41s+fLl+vbbb1VRUaHe3l6VlpZq2bJlIRih9xYsWKBr166prKxMkrRnzx4tXbpU0dHRksz/QPFUXzj1UfKvVlP66K1w66kneXl5Onbs2JDldrtdM2fOVH5+vrZt26ZNmzYNOATTqrq6ulRQUKD8/Hw9+eST6ujoUEdHh5xOZ0T1FdYRTu87sj48+iiR9f3Cra/ukPXW76mts7PTOfzTYAXx8fGqq6sbcNGGrKws7d69W/n5+UOe/95772nbtm1KTk7W2LFjderUqYAfBhhoR44c0fr165WQkKC+vj4dO3ZM2dnZktzX2tPToxdeeEGffvqp0tLStGbNGm3dulUxMdY708RdfeHWR8n3Wk3q490M3j7DsaeR4siRI1q9evWQ5XV1dXrsscfoK4KKrDc/I8j68M16ibwPF+GY9Uzsw9yPP/6oCxcuaMGCBUpKSgr1cLzS3Nysmpoa5eXlKS0tLdTDCTh/6jOxj1L493IkTO0pPKOvCAUT33fhng9kPfqZ2le4Z9WeMrEHAAAAAMBgnGMPAAAAAIDBmNgDAAAAAGAwJvYAAAAAABiMiT0AAAAAAAZjYg8AAAAAgMGY2AMAAAAAYDAm9gCC5vDhw0pNTVVzc7Nr2bPPPqv58+fr2rVrIRwZAAAIBLIesAYm9gCCprCwUDNmzNBbb70lSdq1a5e++uorlZeXKzk5OcSjAwAAI0XWA9YQE+oBAAhfNptNxcXFKioq0oQJE/TOO+/o+PHjysjIkCStXr1alZWVWrJkicrKykI8WgAA4CuyHrAGW2dnpzPUgwAQ3h5++GGdO3dOn3/+uRYuXOhaXlFRoc7OTpWVlRH2AAAYjKwHQotD8QEE1YkTJ1RfX6+bN2/q/vvvH/C7xYsXKyEhIUQjAwAAgUDWA6HHxB5A0NTW1uqZZ57R3r17tWzZMpWUlIR6SAAAIIDIesAaOMceQFA0NjZq5cqVeumll7RmzRplZWVp4cKFqq2tVU5OTqiHBwAARoisB6yDb+wBBNyVK1f01FNPafny5dq6daskKScnR0888YSKi4tDOzgAADBiZD1gLXxjDyDgUlNTVVNTM2T54cOHQzAaAAAQaGQ9YC1cFR9AyKxYsUKnT59WZ2enUlJS9PHHHys3NzfUwwIAAAFC1gP3BhN7AAAAAAAMxjn2AAAAAAAYjIk9AAAAAAAGY2IPAAAAAIDBmNgDAAAAAGAwJvYAAAAAABiMiT0AAAAAAAZjYg8AAAAAgMGY2AMAAAAAYDAm9gAAAAAAGIyJPQAAAAAABmNiDwAAAACAwZjYAwAAAABgsP8HUUP8mn/2Q14AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.rcParams[\"font.sans-serif\"] = \" SimHei\"\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False\n",
    "\n",
    "fig, ax = plt.subplots(1,2, figsize=(12,4),\n",
    "                      facecolor=\"whitesmoke\",\n",
    "                      edgecolor=\"gray\")\n",
    "\n",
    "ax[0].contourf(X_moon_mesh,Y_moon_mesh,Z_moon_mesh,cmap=plt.cm.PiYG)\n",
    "for clslist,color,label in zip(y_moon_class,[\"blue\",\"orange\"],[\"class-0\",\"class-1\"]):\n",
    "    ax[0].scatter(clslist[:,0],clslist[:,1],marker=\"*\",color=color,lw=.5, label=label)\n",
    "ax[0].set_title(\"Single Decision Tree\")\n",
    "ax[0].set_xlabel(r\"$x_1$\")\n",
    "ax[0].set_ylabel(r'$x_2$')\n",
    "ax[0].legend(loc=\"best\")\n",
    "\n",
    "ax[1].contourf(X_moon_mesh,Y_moon_mesh,Z_moon_bag_mesh,cmap=plt.cm.PiYG)\n",
    "for clslist,color,label in zip(y_moon_class,[\"blue\",\"orange\"],[\"class-0\",\"class-1\"]):\n",
    "    ax[1].scatter(clslist[:,0],clslist[:,1],marker=\"*\",color=color,lw=.5, label=label)\n",
    "ax[1].set_title(\"Bagging Decision Tree\")\n",
    "ax[1].set_xlabel(r\"$x_1$\")\n",
    "ax[1].set_ylabel(r'$x_2$')\n",
    "ax[1].legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26264806-6210-4e55-926e-8c5c252900c0",
   "metadata": {},
   "source": [
    "### oob Evaluation\n",
    "1. oob stands for out-of-bag if <font color=maroon><b>bootstrap</b></font> is set <font color=blue><b>True</b></font> and <font color=maroon><b>oob_score</b></font> is set <font color=blue><b>True</b></font> in BaggingClassifier, samples will be drawn from dataset with replacenment\n",
    "2. this means that about 37% data will not be used in ensemble precess, and there's no need to perform cross-validation process afterwards.\n",
    "3. <font color=maroon><b>oob_decisioin_function_</b></font> variable will output predict_proba() of each trainig instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "34cf1743-2213-47ab-a7f0-b7a24e0cc9de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9952941176470588\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.datasets import make_moons\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_moon, y_moon = make_moons(n_samples=1000, shuffle=True,\n",
    "                       random_state=42, noise=0.05)\n",
    "X_moon_train, X_moon_test, y_moon_train, y_moon_test = train_test_split(X_moon, y_moon, test_size=0.15)\n",
    "\n",
    "bag_cross_clf = BaggingClassifier(\n",
    "                DecisionTreeClassifier(), n_estimators=500,\n",
    "                bootstrap=True, n_jobs=1, oob_score=True)\n",
    "bag_cross_clf.fit(X_moon_train,y_moon_train)\n",
    "print(bag_cross_clf.oob_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d15748d1-3416-46a7-8004-1a728562aadf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "y_moon_pred = bag_cross_clf.predict(X_moon_test)\n",
    "acc_moon = accuracy_score(y_moon_test,y_moon_pred)\n",
    "print(acc_moon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7bf22876-4863-4b8b-b6c3-2fdfe833f82e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(850, 2)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       ...,\n",
       "       [0.        , 1.        ],\n",
       "       [0.83040936, 0.16959064],\n",
       "       [0.        , 1.        ]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(bag_cross_clf.oob_decision_function_.shape)\n",
    "bag_cross_clf.oob_decision_function_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8baa8ac5-9783-4e24-8e7a-faed94dc780f",
   "metadata": {},
   "source": [
    "### RandomForest\n",
    "1. sklearn.ensemble <font color=maroon><b>RandomForestClassifier | RandomForestRegressor</b></font>\n",
    "2. sklearn.ensemble <font color=maroon><b>ExtraTreesClassifier | ExtraTreesClassifier</b></font>\n",
    "3. two differences between ExtraTrees and RandomForest is that ExtraTrees will take randomized features for training and will use randomized thresholds to train pick results. This is typically trading more bias for a lower variance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56b83279-afa4-43d7-9146-05995decaaeb",
   "metadata": {},
   "source": [
    "### Feature Importance\n",
    "1. RandomForestClassifier will calculate average depth of each features, the more important one feature is, the closer it will be to the root node. Thus less important features would appear at leaf node or near leaf nodes\n",
    "2. RandomForestClassifier provides feature_importances_ variable, which will print feature importance by its average depth."
   ]
  },
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    "## Boosting\n",
    "1. train predictors sequentially, each trying to correct tis predecessor.\n",
    "2. most popular are: AdaBoost(Adaptive Boosting) and Gradient Boosting"
   ]
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    "### AdaBoost\n",
    "#### weight update\n",
    "> first, set weight of each instance as $\\frac{1}{m}$, where $m$ is the total number of instances\n",
    "> then, train one predictor, calculate error rate via following formula:\n",
    "\n",
    "$$\n",
    "    r_j = \\frac{\\displaystyle\\sum_{\\substack{i=1 \\\\ \\hat y^{(i)} \\ne y_j^{(i)}}}^{m}{w^{(i)}}}{\\displaystyle\\sum_{i=1}^{m}{w^{(i)}}}\n",
    "$$\n",
    "\n",
    "where index $j$ stands for jth predictor and superscript $(i)$ stands for ith instance. $w$ is the weight of ith instance, which is originally set to $\\frac{1}{m}$\n",
    "\n",
    "> then calculate predictor weights for each predictor using following formula\n",
    "\n",
    "$$\n",
    "    \\alpha_j = \\eta \\log\\frac{1-r_j}{r_j}\n",
    "$$\n",
    "\n",
    "> last apply weight update rule to each wrongly classified instance\n",
    "\n",
    "$$\n",
    "w^{(i)} = \n",
    "\\left\\{\n",
    "\\begin{array}{ll}\n",
    "w^{(i)} &, if \\space \\hat y_j^{(i)} = y^{(i)} \\\\\n",
    "w^{(i)}e^{\\alpha_j} &, if \\space \\hat y_j^{(i)} \\ne y^{(i)} \\\\\n",
    "for \\space i = 1,2,\\cdots, m\n",
    "\\end{array}\n",
    "\\right .\n",
    "$$\n",
    "\n",
    "> finally all the instance weights are normalized, divided by $\\sum_{i=1}^{m}{w^{(i)}}$\n",
    "\n",
    "#### make prediction\n",
    "> the predicted class will receive the majority of weighted votes\n",
    "\n",
    "$$\n",
    "    \\hat y(x) = \\mathop{\\arg\\max}\\limits_{k} \\displaystyle{\\sum_{\\substack{j=1 \\\\ \\hat y_j(x)=k}}^{N}{\\alpha_j}}\n",
    "$$\n",
    "\n",
    "where $N$ is the number of predictors, $k$ is the predicted class, <br><font color=red><b>the formula above just calculates predictor weights of all predictors that predicting one same class k<b></font>"
   ]
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   "source": [
    "#### sklearn implementation\n",
    "1. algorithm is called SAMME (Stagewise Additive Modeling Using a Multiclass Exponential loss function)\n",
    "2. if classifier has <font color=brown><b>predict_proba()</b></font> method, we could use algorithm named SAMME.R, this method relies on probabilities rather than predictions and generally performs better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5098dc87-051f-452f-8cbd-246379122956",
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    {
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       "1.0"
      ]
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     "execution_count": 19,
     "metadata": {},
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   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "ada_clf = AdaBoostClassifier(\n",
    "            DecisionTreeClassifier(max_depth=1), n_estimators=200,\n",
    "            algorithm=\"SAMME.R\", learning_rate=0.5)\n",
    "ada_clf.fit(X_moon_train,y_moon_train)\n",
    "y_moon_ada_pred = ada_clf.predict(X_moon_test)\n",
    "accuracy_score(y_moon_test, y_moon_ada_pred)"
   ]
  },
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   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "### Gredient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3827e8f-8a8a-4b78-b136-73d99918031e",
   "metadata": {},
   "outputs": [],
   "source": []
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